Abstract
Salient Object Detection (SOD) in natural images is an active research area with burgeoning applications across diverse disciplines such as object recognition, image compression, video summarization, object discovery, image retargetting etc. Most salient object detection methods model this problem as a binary segmentation problem where firstly a saliency map is found which highlights the salient pixels and suppresses the background pixels in an image. Secondly, some threshold is applied to obtain the binary segmentation from the saliency map. Thus, thresholding is an important ingredient of salient object detection methods and affects the SOD performance. In this paper, we provide a comprehensive review of various thresholding methods in literature employed for SOD. We have developed a taxonomy of thresholding methods which shall be useful to the researchers and practitioners working in this fascinating research field. Further, we also discuss unexplored thresholding approaches which can be employed in SOD. Various existing and proposed performance measures to analyze SOD methods that depend on thresholding are also presented. Experiments on popular thresholding methods have also been carried out to show the dependence of qualitative and quantitative performance on thresholding.
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References
Abak AT, Baris U, Sankur B (1997) The performance evaluation of thresholding algorithms for optical character recognition. In: Proceedings of the fourth international conference on document analysis and recognition, 1997, vol 2, pp 697–700. https://doi.org/10.1109/ICDAR.1997.620597
Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. In: Proceedings of the 6th international conference on computer vision systems. Springer-Verlag, ICVS’08, pp 66–75
Achanta R, Sheila S H, Francisco J E, Su̇sstrunk S (2009) Frequency-tuned salient region detection. In: 2009 IEEE computer society conference on computer vision and pattern recognition (CVPR 2009), Miami, pp 1597–1604, DOI https://doi.org/10.1109/CVPRW.2009.5206596, (to appear in print)
Alexe B, Deselaers T, Ferrari V (2010) What is an object?. In: The twenty-third IEEE conference on computer vision and pattern recognition, CVPR 2010. San Francisco, pp 73–80. https://doi.org/10.1109/CVPR.2010.5540226
Arya R, Singh N, Agrawal R K (2015) A novel hybrid approach for salient object detection using local and global saliency in frequency domain. Multimed Tools Appl 1–21. https://doi.org/10.1007/s11042-015-2750-y
Avidan S, Shamir A (2007) Seam carving for content-aware image resizing. ACM Trans Graph 26:3. https://doi.org/10.1145/1276377.1276390
Avraham T, Lindenbaum M (2010) Esaliency (extended saliency): meaningful attention using stochastic image modeling. IEEE Trans Pattern Anal Mach Intell 32(4):693–708. https://doi.org/10.1109/TPAMI.2009.53
Bhanu B (1986) Automatic target recognition: state of the art survey. IEEE Trans Aerosp Electron Syst AES-22(4):364–379. https://doi.org/10.1109/TAES.1986.310772
Borji A (2012) Boosting bottom-up and top-down visual features for saliency estimation. In: 2012 IEEE conference on computer vision and pattern recognition. Providence, pp 438–445, DOI https://doi.org/10.1109/CVPR.2012.6247706, (to appear in print)
Borji A (2015) What is a salient object? A dataset and a baseline model for salient object detection. IEEE Trans Image Processing 24(2):742–756. https://doi.org/10.1109/TIP.2014.2383320
Borji A, Itti L (2012) Exploiting local and global patch rarities for saliency detection. In: 2012 IEEE Conference on computer vision and pattern recognition. Providence, pp 478–485, DOI https://doi.org/10.1109/CVPR.2012.6247711, (to appear in print)
Borji A, Itti L (2013) State-of-the-art in visual attention modeling. IEEE Trans PAMI 35(1):185–207. https://doi.org/10.1109/TPAMI.2012.89
Borji A, Sihite DN, Itti L (2013) What stands out in a scene? A study of human explicit saliency judgment. Vis Res 91:62–77. https://doi.org/10.1016/j.visres.2013.07.016
Borji A, Sihite D N, Itti L (2014) What/where to look next? Modeling top-down visual attention in complex interactive environments. IEEE Trans Syst Man Cybern Syst 44(5):523–538. https://doi.org/10.1109/TSMC.2013.2279715
Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124 –1137. https://doi.org/10.1109/TPAMI.2004.60
Boykovi Y, Lea G F (2006) Graph cuts and efficient n-d image segmentation. Int J Comput Vis 70(2):109–131. https://doi.org/10.1007/s11263-006-7934-5
Bruce N, Tsotsos J (2006) Saliency based on information maximization. Adv Neural Inf Process Syst 18:155–162
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Chang KY, Liu TL, Chen HT, Lai SH (2011) Fusing generic objectness and visual saliency for salient object detection. In: Proceedings of the 2011 international conference on computer vision ICCV ’11. IEEE Computer Society, Washington, DC, pp 914–921, DOI https://doi.org/10.1109/ICCV.2011.6126333, (to appear in print)
Chen L Q, Xie X, Fan X, Ma W Y, Zhang H J, H Q Zhou H Q (2003) A visual attention model for adapting images on small displays. Multimed Syst 9(4):353–364. https://doi.org/10.1007/s00530-003-0105-4
Chen T, Lin L, Liu L, Luo X, Li X (2015) DISC: deep image saliency computing via progressive representation learning. CoRR arXiv:1511.04192
Cheng M M, Warrell J, Lin W Y, Zheng S, Vineet V, Crook N (2013) Efficient salient region detection with soft image abstraction. In: IEEE International conference on computer vision, ICCV 2013. Sydney, pp 1529–1536, DOI https://doi.org/10.1109/ICCV.2013.193, (to appear in print)
Cheng M M, Mitra N J, Huang X, Torr P H S, Hu S M (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37 (3):569–582. https://doi.org/10.1109/TPAMI.2014.2345401
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1023/A:1022627411411
Deravi F, Pal S K (1983) Grey level thresholding using second-order statistics. Pattern Recogn Lett 1(5–6):417–422. https://doi.org/10.1016/0167-8655(83)90080-6
Dong L, Lin W, Fang Y, Wu S, Seah HS (2014) Saliency detection in computer rendered images based on object-level contrast. J Vis Commun Image Represent 25(3):525–533. https://doi.org/10.1016/j.jvcir.2013.11.009
Du S, Chen S (2014) Salient object detection via random forest. IEEE Signal Process Lett 21(1):51–54. https://doi.org/10.1109/LSP.2013.2290547
Fan Q, Qi C (2014) Two-stage salient region detection by exploiting multiple priors. J Vis Commun Image Represent 25(8):1823–1834. https://doi.org/10.1016/j.jvcir.2014.09.003
Fan Q, Qi C (2016) Saliency detection based on global and local short-term sparse representation. Neurocomput 175(PA):81–89. https://doi.org/10.1016/j.neucom.2015.10.030
Fareed M M S, Ahmed G, Chun Q (2015) Salient region detection through sparse reconstruction and graph-based ranking. J Vis Comun Image Represent 32(C):144–155. https://doi.org/10.1016/j.jvcir.2015.08.002
Fekete G, Eklundh JO, Rosenfeld A (1981) Relaxation: evaluation and applications. IEEE Trans Pattern Anal Mach Intell 3(4):459–469. https://doi.org/10.1109/TPAMI.1981.4767131
Fernandez X (2000) Implicit model-oriented optimal thresholding using the komolgorov-smirnov similarity measure. In: Proceedings 15th international conference on pattern recognition, 2000, vol 1, pp 466–469. https://doi.org/10.1109/ICPR.2000.905377
Frintrop S, García GM, Cremers AB (2014) A cognitive approach for object discovery. In: 22nd international conference on pattern recognition, ICPR 2014. Stockholm, pp 2329–2334. https://doi.org/10.1109/ICPR.2014.404 https://doi.org/10.1109/ICPR.2014.404
Fu K, Gong C, Yang J, Zhou Y, Gu I Y H (2013) Superpixel based color contrast and color distribution driven salient object detection. Signal Process Image Commun 28(10):1448–1463. https://doi.org/10.1016/j.image.2013.07.005
Fu K, Gong C, Gu I Y H, Yang J, He X (2014) Spectral salient object detection. In: IEEE international conference on multimedia and expo, ICME 2014. Chengdu, pp 1–6. https://doi.org/10.1109/ICME.2014.6890142
Fu K, Gong C, Gu I Y H, Yang J (2015) Normalized cut-based saliency detection by adaptive multi-level region merging. IEEE Trans Image Process 24(12):5671–5683. https://doi.org/10.1109/TIP.2015.2485782
Gao HY, Lam KM (2014) Saliency detection based on adaptive dog and distance transform. In: 2014 IEEE international symposium on circuits and systems (ISCAS), pp 534–537. https://doi.org/10.1109/ISCAS.2014.6865190
Gao HY, Lam KM (2014) Salient object detection using octonion with bayesian inference. In: 2014 IEEE international conference on image processing (ICIP), pp 3292–3296. https://doi.org/10.1109/ICIP.2014.7025666
Gao D, Vasconcelos N (2004) Discriminant saliency for visual recognition from cluttered scenes. Adv Neural Inf Process Syst 17:481–488. [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia Canada]
Goferman S, Manor L Z, Tal A (2012) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34(10):1915–1926. https://doi.org/10.1109/TPAMI.2011.272
Goldberg C, Chen T, Zhang F L, Shamir A, Hu S M (2012) Data-driven object manipulation in images. Comput Graph Forum 31(2pt1):265–274. https://doi.org/10.1111/j.1467-8659.2012.03005.x
Guo C, Zhang L (2010) A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185–198. https://doi.org/10.1109/TIP.2009.2030969
Guo C, Ma Q, Zhang L (2008) Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: IEEE Conference on computer vision and pattern recognition, 2008. CVPR 2008, pp 1–8, DOI https://doi.org/10.1109/CVPR.2008.4587715, (to appear in print)
Guo M, Zhao Y, Zhang C, Chen Z (2014) Fast object detection based on selective visual attention. Neurocomputing 144:184–197. https://doi.org/10.1016/j.neucom.2014.04.054
Harel J, Koch C, Perona P (2006) Graph-based visual saliency. In: Proceedings of the twentieth annual conference on neural information processing systems advances in neural information processing systems, vol 19. Vancouver, pp 545–552
He S L, Lau R W H, Liu W H, YQZ (2015) Supercnn: a superpixelwise convolutional neural network for salient object detection. Int J Comput Vis 115 (3):330–344. https://doi.org/10.1007/s11263-015-0822-0
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: IEEE Conference on computer vision and pattern recognition (CVPR07). IEEE Computer Society, pp 1–8. https://doi.org/10.1109/CVPR.2007.383267
Hou X, Zhang L (2009) Dynamic visual attention: searching for coding length increments. In: Advances in neural information processing systems, vol 21. Curran Associates Inc., pp 681–688
Hu X, Shen J, Shan J, Pan L (2013) Local edge distributions for detection of salient structure textures and objects. IEEE Geosci Remote Sensing Lett 10(3):466–470. https://doi.org/10.1109/LGRS.2012.2210188
Itti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Process 13(10):1304–1318. https://doi.org/10.1109/TIP.2004.834657
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259. https://doi.org/10.1109/34.730558
Jawahar C, Biswas P, Ray A (1997) Investigations on fuzzy thresholding based on fuzzy clustering. Pattern Recog 30(10):1605–1613. https://doi.org/10.1016/S0031-3203(97)00004-6
Ji Q G, Fang Z D, Xie Z H, Lu Z M (2013) Video abstraction based on the visual attention model and online clustering. Image Commun 28(3):241–253. https://doi.org/10.1016/j.image.2012.11.008
Jia Y, Han M (2013) Category-independent object-level saliency detection. In: IEEE International conference on computer vision, ICCV 2013. Sydney, pp 1761–1768. https://doi.org/10.1109/ICCV.2013.221
Jia C, Qi J, Li X, Lu H (2016) Saliency detection via a unified generative and discriminative model. Neurocomputing 173(Part 2):406–417. https://doi.org/10.1016/j.neucom.2015.03.122
Jian M, Lam K M, Dong J, Shen L (2015) Visual-patch-attention-aware saliency detection. IEEE Trans Cybern 45(8):1575–1586. https://doi.org/10.1109/TCYB.2014.2356200
Jiang Z, Davis L S (2013) Submodular salient region detection. In: 2013 IEEE conference on computer vision and pattern recognition. Portland, pp 2043–2050, DOI https://doi.org/10.1109/CVPR.2013.266, (to appear in print)
Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li S (2013) Salient object detection: a discriminative regional feature integration approach. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 2083–2090. https://doi.org/10.1109/CVPR.2013.271
Jin Z, Han J, Zhang Y, Bai L (2015) Saliency model based on a discrete centre-surround. Electron. Lett. 51(8):626–628. https://doi.org/10.1049/el.2014.4316
Johannsen G, Bille J (1982) A threshold selection method using information measures. In: Proceedings of the 6th international conference on pattern recognition, pp 140–143
Ju R, Liu Y, Ren T, Ge L, Wu G (2015) Depth-aware salient object detection using anisotropic center-surround difference. Signal Process Image Commun 38:115–126. https://doi.org/10.1016/j.image.2015.07.002 Recent Advances in Saliency Models, Applications and Evaluations
Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: IEEE international conference on computer vision (ICCV), pp 2106–2113. https://doi.org/10.1109/ICCV.2009.5459462
Kamel M, Zhao A (1993) Extraction of binary character/graphics images from grayscale document images. CVGIP: Graph Models Image Process 55(3):203–217. https://doi.org/10.1006/cgip.1993.1015
Kannan R, Ghinea G, Swaminathan S (2015) Salient region detection using patch level and region level image abstractions. IEEE Signal Process Lett 22(6):686–690. https://doi.org/10.1109/LSP.2014.2366192
Kapur J, Sahoo P, Wong A (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285. https://doi.org/10.1016/0734-189X(85)90125-2
Karpathy A, Miller S D, Li F F (2013) Object discovery in 3d scenes via shape analysis. In: 2013 IEEE International conference on robotics and automation. Karlsruhe, pp 2088–2095, https://doi.org/10.1109/ICRA.2013.6630857, (to appear in print)
Khuwuthyakorn P, Kelly A R, Zhou J (2010) Computer vision – ECCV 2010: 11th European conference on computer vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part II. Chap object of interest detection by saliency learning. Springer, Berlin, pp 636–649, DOI https://doi.org/10.1007/978-3-642-15552-9-46, (to appear in print)
Kienzle W, Franz M, Schölkopf B, Wichmann F (2009) Center-surround patterns emerge as optimal predictors for human saccade targets. J Vis 9(5:7):1–15
Kim J, Lee H, Kim J (2013) A novel method for salient object detection via compactness measurement. In: IEEE International conference on image processing, ICIP 2013. Melbourne, pp 3426–3430, DOI https://doi.org/10.1109/ICIP.2013.6738707, (to appear in print)
Kim J, Han D, Tai YW, Kim J (2014) Salient region detection via high-dimensional color transform. In: 2014 IEEE Conference on computer vision and pattern recognition, pp 883–890. https://doi.org/10.1109/CVPR.2014.118
Kirby R L, Rosenfeld A (1979) A note on the use of (gray level, local average gray level) space as an aid in threshold selection. IEEE Trans Syst Man Cybern 9(12):860–864. https://doi.org/10.1109/TSMC.1979.4310138 https://doi.org/10.1109/TSMC.1979.4310138
Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recogn 19(1):41–47. https://doi.org/10.1016/0031-3203(86)90030-0
Klein D A, Frintrop S (2011) Center-surround divergence of feature statistics for salient object detection. In: IEEE International conference on computer vision, ICCV 2011. Barcelona, pp 2214–2219, DOI https://doi.org/10.1109/ICCV.2011.6126499, (to appear in print)
Koch C, Ullman S (1985) Shifts in selective visual attention: towards the underlying neural circuitry. Hum Neurobiol 4:219–227. https://doi.org/10.1007/978-94-009-3833-5-5
Koch C, Ullman S (1987) Matters of intelligence: conceptual structures in cognitive neuroscience. Chap shifts in selective visual attention: towards the underlying neural circuitry. Springer, Netherlands, pp 115–141, DOI https://doi.org/10.1007/978-94-009-3833-5-5, (to appear in print)
Kong L, Duan L, Yang W, Dou Y (2015) Salient region detection: an integration approach based on image pyramid and region property. IET Comput Vis 9(1):85–97. https://doi.org/10.1049/iet-cvi.2013.0285
Ksantini R, Boufama B, Memar S (2013) A new efficient active contour model without local initializations for salient object detection. EURASIP J Image Video Process 2013:40. https://doi.org/10.1186/1687-5281-2013-40
Kumar N, Singh M, Govil M C, Pilli E S, Jaiswal A (2016) Salient object detection in noisy images. In: Proceedings of the 29th Canadian conference on artificial intelligence on advances in artificial intelligence - 9673. Springer-Verlag New York, Inc., New York, pp 109–114. https://doi.org/10.1007/978-3-319-34111-8-15
Kwak S Y, Ko B, Byun H (2004) Automatic salient-object extraction using the contrast map and salient points. In: 5th Pacific Rim conference on multimedia. Tokyo, pp 138–145. https://doi.org/10.1007/978-3-540-30542-2-18
Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning ICML ’01. Morgan Kaufmann Publishers Inc., San Francisco, pp 282–289
Lang C, Feng J, Feng S, Wang J, Yan S (2016) Dual low-rank pursuit: learning salient features for saliency detection. IEEE Trans Neural Netw Learn Syst 27(6):1190–1200. https://doi.org/10.1109/TNNLS.2015.2513393
Leitner J, Harding S, Chandrashekhariah P, Frank M, Förster A, Triesch J, Schmidhuber J (2013) Learning visual object detection and localisation using icvision. Biolog Insp Cogn Architect 5(0):29–41. https://doi.org/10.1016/j.bica.2013.05.009
Leung C, Lam F (1996) Performance analysis for a class of iterative image thresholding algorithms. Pattern Recogn 29(9):1523–1530. https://doi.org/10.1016/0031-3203(96)00009-X
Leung C, Lam F (1998) Maximum segmented image information thresholding. Graph Models Image Process 60(1):57–76. https://doi.org/10.1006/gmip.1997.0455
Li Z, Chen J (2008) On semantic object detection with salient feature. In: Advances in visual computing, 4th international symposium, ISVC December 1-3, 2008. Proceedings, Part II, pp. 782–791. https://doi.org/10.1007/978-3-540-89646-3-77
Li H, Ngan K N (2011) A co-saliency model of image pairs. IEEE Trans Image Process 20(12):3365–3375. https://doi.org/10.1109/TIP.2011.2156803
Li C, Tam P (1998) An iterative algorithm for minimum cross entropy thresholding. Pattern Recogn Lett 19 (8):771–776. https://doi.org/10.1016/S0167-8655(98)00057-9
Li X, Li Y, Shen C, Dick A, Hengel AVD (2013) Contextual hypergraph modeling for salient object detection. In: Proceedings of the 2013 IEEE international conference on computer vision ICCV ’13. Washington, DC, pp 3328–3335, DOI https://doi.org/10.1109/ICCV.2013.413, (to appear in print)
Li Y, Fu K, Zhou L, Qiao Y, Yang J, Li B (2014) Saliency detection based on extended boundary prior with foci of attention. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2798–2802. https://doi.org/10.1109/ICASSP.2014.6854110
Li C, Hu Z, Xiao L, Pan Z (2015) Saliency detection via low-rank reconstruction from global to local. In: Chinese automation congress (CAC) 2015, pp 669–673. https://doi.org/10.1109/CAC.2015.7382582
Li J, Meng F, Zhang Y (2015) Saliency detection using a background probability model. In: 2015 IEEE international conference on image processing (ICIP), pp 2189–2193. https://doi.org/10.1109/ICIP.2015.7351189
Li S, Lu H, Lin Z L, Shen X, Price B L (2015) Adaptive metric learning for saliency detection. IEEE Trans Image Process 24(11):3321–3331
Liang Z, Chi Z, Fu H, Feng D D (2012) Salient object detection using content-sensitive hypergraph representation and partitioning. Pattern Recogn 45 (11):3886–3901. https://doi.org/10.1016/j.patcog.2012.04.017
Lin M, Zhang C, Chen Z (2015) Global feature integration based salient region detection. Neurocomputing 159:1–8. https://doi.org/10.1016/j.neucom.2015.02.050
Lin M, Zhang C, Chen Z (2016) Predicting salient object via multi-level features. Neurocomputing 205:301–310. https://doi.org/10.1016/j.neucom.2016.04.036. http://www.sciencedirect.com/science/article/pii/S0925231216303010
LItti, Baldi P (2009) Bayesian surprise attracts human attention. Vis Res 49(10):1295–1306. https://doi.org/10.1016/j.visres.2008.09.007. Visual attention: psychophysics, electrophysiology and neuroimaging
Liu F, Gleicher M (2006) Region enhanced scale-invariant saliency detection. In: Proceedings of the 2006 IEEE international conference on multimedia and expo, ICME 2006, pp 1477–1480 https://doi.org/10.1109/ICME.2006.262821
Liu H, Heynderickx I (2009) Studying the added value of visual attention in objective image quality metrics based on eye movement data. In: 2009 16th IEEE international conference on image processing (ICIP), pp 3097–3100. https://doi.org/10.1109/ICIP.2009.5414466
Liu J, Wang S (2015) Salient region detection via simple local and global contrast representation. Neurocomputing 147:435–443. https://doi.org/10.1016/j.neucom.2014.06.041. Advances in Self-Organizing Maps Subtitle of the special issue: Selected Papers from the Workshop on Self-Organizing Maps 2012 (WSOM 2012)
Liu T, Sun J, Zheng N, Tang X, Shum HY (2007) Learning to detect a salient object. In: IEEE computer society conference on computer vision and pattern recognition, pp 1–8. https://doi.org/10.1109/CVPR.2007.383047 https://doi.org/10.1109/CVPR.2007.383047
Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum H Y (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33 (2):353–367. https://doi.org/10.1109/TPAMI.2010.70
Liu Q, Han T, Sun Y, Chu Z, Shen Z B (2012) A two step salient objects extraction framework based on image segmentation and saliency detection. Multimed Tools Appl 67(1):231–247. https://doi.org/10.1007/s11042-012-1077-1
Liu Z, Zou W, Meur O L (2014) Saliency tree: a novel saliency detection framework. IEEE Trans Image Process 23(5):1937–1952. https://doi.org/10.1109/TIP.2014.2307434
Liu R, Cao J, Lin Z, Shan S (2014) Adaptive partial differential equation learning for visual saliency detection. In: 2014 IEEE conference on computer vision and pattern recognition, pp 3866–3873. https://doi.org/10.1109/CVPR.2014.494
Liu Y, Cai Q, Zhu X, Cao J, Li H (2015) Saliency detection using two-stage scoring. In: 2015 IEEE International conference on image processing (ICIP), pp 4062–4066. https://doi.org/10.1109/ICIP.2015.7351569 https://doi.org/10.1109/ICIP.2015.7351569
Liu Z, Gu G, Chen C, Cui D, Lin C (2016) Background priors based saliency object detection. In: 2016 Asia-Pacific signal and information processing association annual summit and conference (APSIPA), pp 1–4. https://doi.org/10.1109/APSIPA.2016.7820744
Lu S, Mahadevan V, Vasconcelos N (2014) Learning optimal seeds for diffusion-based salient object detection. In: 2014 IEEE conference on computer vision and pattern recognition, pp 2790–2797. https://doi.org/10.1109/CVPR.2014.357
Lu H, Li X, Zhang L, Ruan X, Yang M H (2016) Dense and sparse reconstruction error based saliency descriptor. IEEE Trans Image Process 25 (4):1592–1603. https://doi.org/10.1109/TIP.2016.2524198
Luo Y, Yuan J, Xue P, Tian Q (2011) Saliency density maximization for efficient visual objects discovery. IEEE Trans Circ Syst Vid Techn 21(12):1822–1834. https://doi.org/10.1109/TCSVT.2011.2147230
Luo W, Li H, Liu G, Ngan K N (2012) Global salient information maximization for saliency detection. Sig Proc Image Comm 27(3):238–248. https://doi.org/10.1016/j.image.2011.10.004
Ma Y F, Zhang H J (2003) Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the eleventh ACM international conference on multimedia, pp 374–381. https://doi.org/10.1145/957013.957094
Ma Y F, Hua X S, Lu L, Zhang H J (2005) A generic framework of user attention model and its application in video summarization. IEEE Trans Multimed 7(5):907–919. https://doi.org/10.1109/TMM.2005.854410
Ma X, Xie X, Lam K M, Hu J, Zhong Y (2015) Saliency detection based on singular value decomposition. J Vis Comun Image Represent 32(C):95–106. https://doi.org/10.1016/j.jvcir.2015.08.003
Mahmoudi L, Zaart AE (2012) A survey of entropy image thresholding techniques. In: 2012 2nd international conference on advances in computational tools for engineering applications (ACTEA), pp 204–209. https://doi.org/10.1109/ICTEA.2012.6462867
Manipoonchelvi P, Muneeswaran K (2014) Region-based saliency detection. IET Image Process 8(9):519–527. https://doi.org/10.1049/iet-ipr.2013.0434
Manke R, Jalal A S (2015) Poisson-distribution-based approach for salient region detection. Electron Lett 51(1):37–38. https://doi.org/10.1049/el.2014.3334
Marchesotti L, Cifarelli C, Csurka G (2009) A framework for visual saliency detection with applications to image thumbnailing. In: IEEE 12th international conference on computer vision, ICCV 2009. Kyoto, pp 2232-2239. https://doi.org/10.1109/ICCV.2009.5459467
Margolin R, Tal A, Zelnik-Manor L (2013) What makes a patch distinct? In: 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 1139–1146. https://doi.org/10.1109/CVPR.2013.151
Martín RV, Marfil R, Nu̇ṅez P, Bandera A, Hernȧndez FS (2009) A novel approach for salient image regions detection and description. Pattern Recogn Lett 30(16):1464–1476. https://doi.org/10.1016/j.patrec.2009.08.003
Mehrani P, Veksler O (2010) Saliency segmentation based on learning and graph cut refinement. In: Proceedings of the British machine vision conference. BMVA Press, pp 110.1–110.12. https://doi.org/10.5244/C.24.110
Muratov O, Boato G, Natale FGBD (2013) Salient object detection using scene layout estimation. In: 2013 IEEE 15th international workshop on multimedia signal processing (MMSP), pp 390–395. https://doi.org/10.1109/MMSP.2013.6659320
Murthy C A, Pasl S K (1990) Fuzzy thresholding: mathematical framework, bound functions and weighted moving average technique. Pattern Recogn Lett 11 (3):197–206. https://doi.org/10.1016/0167-8655(90)90006-N
Nakagawa Y, Rosenfeld A (1979) Some experiments on variable thresholding. Pattern Recogn 11(3):191–204. https://doi.org/10.1016/0031-3203(79)90006-2
Naqvi S S, Browne W N, Hollitt C (2016) Salient object detection via spectral matting. Pattern Recogn 51(C):209–224. https://doi.org/10.1016/j.patcog.2015.09.026
Navalpakkam V, Itti L (2005) Modeling the influence of task on attention. Vis Res 45(2):205–231. https://doi.org/10.1016/j.visres.2004.07.042
Niblack W (1985) An introduction to digital image processing. Strandberg Publishing Company Birkeroed, Denmark
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076
Pal S, King R, Hashim A (1983) Automatic grey level thresholding through index of fuzziness and entropy. Pattern Recogn Lett 1(3):141–146. https://doi.org/10.1016/0167-8655(83)90053-3
Palumbo P W, Swaminathan P, Srihari S N (1986) Document image binarization: evaluation of algorithms. Proc SPIE Appl Digit Image Process 0697:278–286. https://doi.org/10.1117/12.976229
Peng H, Li B, Ling H, Hu W, Xiong W, Maybank S J (2017) Salient object detection via structured matrix decomposition. IEEE Trans Pattern Anal Mach Intell 39(4):818–832. https://doi.org/10.1109/TPAMI.2016.2562626
Perazzi F, Krȧhenbu̇hl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on computer vision and pattern recognition. Providence, pp 733–740, DOI https://doi.org/10.1109/CVPR.2012.6247743, (to appear in print)
Peters RJ, Itti L (2007) Beyond bottom-up: incorporating task-dependent influences into a computational model of spatial attention. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1–8. https://doi.org/10.1109/CVPR.2007.383337
Pun T (1980) A new method for grey-level picture thresholding using the entropy of the histogram. Signal Process 2(3):223–237. https://doi.org/10.1016/0165-1684(80)90020-1
Pun T (1981) Entropic thresholding, a new approach. Comput Graph Image Process 16(3):210–239. https://doi.org/10.1016/0146-664X(81)90038-1
Qin C, Zhang G, Zhou Y, Tao W, Cao Z (2014) Integration of the saliency-based seed extraction and random walks for image segmentation. Neurocomput 129:378–391. https://doi.org/10.1016/j.neucom.2013.09.021
Qi S, Yu J G, Ma J, Li Y, Tian J (2015) Salient object detection via contrast information and object vision organization cues. Neurocomput 167(C):390–405. https://doi.org/10.1016/j.neucom.2015.04.055
Qi W, Han J, Zhang Y, Bai L (2015) Saliency detection via boolean and foreground in a dynamic Bayesian framework. Vis Comput 1–12. https://doi.org/10.1007/s00371-015-1176-x
Qi W, Cheng M M, Borji A, Lu H, Bai L F (2016) Saliencyrank: two-stage manifold ranking for salient object detection. Comput Vis Media 1(4):309–320. https://doi.org/10.1007/s41095-015-0028-y
Qin Y, Lu H, Xu Y, Wang H (2015) Saliency detection via cellular automata. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 110–119. https://doi.org/10.1109/CVPR.2015.7298606
Rahtu E, Kannala J, Salo M, Heikkilä J (2010) Segmenting salient objects from images and videos. In: Proceedings of the 11th European conference on computer vision: Part V, ECCV’10. Berlin, Heidelberg, pp 366–379
Ramar K, Arumugam S, Sivanandam S, Ganesan L, Manimegalai D (2000) Quantitative fuzzy measures for threshold selection. Pattern Recogn Lett 21(1):1–7. https://doi.org/10.1016/S0167-8655(99)00120-8
Ramström O, Christensen HI (2002) Visual attention using game theory. In: Biologically motivated computer vision second international workshop, BMCV 2002. Tübingen, Germany, November 22-24, 2002, Proceedings, pp 462–471. https://doi.org/10.1007/3-540-36181-2-46
Rao RP, Gregory JZ, Hayhoe MM, Dana HB (2002) Eye movements in iconic visual search. Vis Res 42(11):1447–1463. https://doi.org/10.1016/S0042-6989(02)00040-8
Ren Y F, Mu Z C (2014) Salient object detection based on global contrast on texture and color. In: 2014 international conference on machine learning and cybernetics, vol 1, pp 7–12. https://doi.org/10.1109/ICMLC.2014.7009083
Ren Z, Gao S, Chia L T, Tsang I W H (2014) Region-based saliency detection and its application in object recognition. IEEE Trans Circ Syst Vid Technol 24(5):769–779. https://doi.org/10.1109/TCSVT.2013.2280096
Rijsbergen C J V (1979) Information retrieval, 2nd edn. Butterworth-Heinemann, Newton
Rodhetbhai W, Lewis PH (2007) Salient region filtering for background subtraction. In: Proceedings of the 9th international conference on advances in visual information systems, VISUAL’07, pp 126–135
Rosenfeld A, Torre P D L (1983) Histogram concavity analysis as an aid in threshold selection. IEEE Trans Syst Man Cybern SMC-13(2):231–235. https://doi.org/10.1109/TSMC.1983.6313118
Rosin P L (2009) A simple method for detecting salient regions. Pattern Recogn 42(11):2363–2371. https://doi.org/10.1016/j.patcog.2009.04.021
Rother C, Kolmogorov V, Blake A (2004) “grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314. https://doi.org/10.1145/1015706.1015720
Roy S, Das S (2013) Spatial variance of color and boundary statistics for salient object detection. In: 2013 Fourth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), pp 1–4. https://doi.org/10.1109/NCVPRIPG.2013.6776270
Sahoo P, Wilkins C, Yeager J (1997) Threshold selection using renyi’s entropy. Pattern Recogn 30(1):71–84. https://doi.org/10.1016/S0031-3203(96)00065-9
Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recogn 33:225– 236
Scharfenberger C, Wong A, Fergani K, Zelek JS, Clausi DA (2013) Statistical textural distinctiveness for salient region detection in natural images. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 979–986. https://doi.org/10.1109/CVPR.2013.131
Seo Y, Yoo C D (2014) Salient object detection based on sparse representation with image-specific prior. In: The 18th IEEE international symposium on consumer electronics (ISCE 2014), pp 1–2. https://doi.org/10.1109/ISCE.2014.6884549
Sezan M I (1990) A peak detection algorithm and its application to histogram-based image data reduction. Comput Vis Graph Image Process 49(1):36–51. https://doi.org/10.1016/0734-189X(90)90161-N
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–168. https://doi.org/10.1117/1.1631315
Sezgin M, Taşaltin R (2000) A new dichotomization technique to multilevel thresholding devoted to inspection applications. Pattern Recogn Lett 21 (2):151–161. https://doi.org/10.1016/S0167-8655(99)00142-7 https://doi.org/10.1016/S0167-8655(99)00142-7
Shao L, Brady M (2006) Invariant salient regions based image retrieval under viewpoint and illumination variations. J Vis Commun Image Represent 17(6):1256–1272. https://doi.org/10.1016/j.jvcir.2006.08.002
Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: 2012 IEEE conference on computer vision and pattern recognition. Providence, pp 853–860. https://doi.org/10.1109/CVPR.2012.6247758
Shi J, Yan Q, Xu L, Jia J (2016) Hierarchical image saliency detection on extended cssd. IEEE Trans Pattern Anal Mach Intell 38(4):717–729. https://doi.org/10.1109/TPAMI.2015.2465960
Siagian C, Itti L (2009) Biologically inspired mobile robot vision localization. IEEE Trans Robot 25(4):861–873. https://doi.org/10.1109/TRO.2009.2022424
Singh N, Agrawal R K (2015) Combination of kullback-leibler divergence and manhattan distance measures to detect salient objects. SIViP 9(2):427–435. https://doi.org/10.1007/s11760-013-0457-y
Singh A, Chu CHH, Pratt MA (2014) Multiresolution superpixels for visual saliency detection. In: 2014 IEEE symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP), pp 1–8. https://doi.org/10.1109/CIMSIVP.2014.7013277
Singh N, Arya R, Agrawal R K (2014) A novel approach to combine features for salient object detection using constrained particle swarm optimization. Pattern Recogn 47(4):1731–1739. https://doi.org/10.1016/j.patcog.2013.11.012
Siva P, Russell C, Xiang T, Agapito L (2013) Looking beyond the image: unsupervised learning for object saliency and detection. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 3238–3245. https://doi.org/10.1109/CVPR.2013.416
Sugano Y, Matsushita Y, Sato Y (2010) Calibration-free gaze sensing using saliency maps. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 2667–2674. https://doi.org/10.1109/CVPR.2010.5539984
Sun X, Zhu Z, Liu X, Shang Y, Yu Q (2015) Frequency-spatial domain based salient region detection. Optik - Int J Light Electron Opt 126(9–10):942–949. https://doi.org/10.1016/j.ijleo.2015.03.004
Tang C, Hou C, Wang P, Song Z (2015) Salient object detection using color spatial distribution and minimum spanning tree weight. Multimed Tools Appl 1–16. https://doi.org/10.1007/s11042-015-2622-5
Tang Y, Tong R, Tang M, Zhang Y (2015) Depth incorporating with color improves salient object detection. Vis Comput 32(1):111–121. https://doi.org/10.1007/s00371-014-1059-6
Tong N, Lu H, Zhang L, Ruan X (2014) Saliency detection with multi-scale superpixels. IEEE Signal Process Lett 21(9):1035–1039. https://doi.org/10.1109/LSP.2014.2323407
Tong N, Lu H, Zhang Y, Ruan X (2015) Salient object detection via global and local cues. Pattern Recogn 48(10):3258–3267. https://doi.org/10.1016/j.patcog.2014.12.005
Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12(1):97–136. https://doi.org/10.1016/0010-0285(80)90005-5
Trier O D, Jain A K (1995) Goal-directed evaluation of binarization methods. IEEE Trans Pattern Anal Mach Intell 17(12):1191–1201. https://doi.org/10.1109/34.476511
Tsai W H (1995) Moment-preserving thresholding: a new approach. In: O’Gorman L, Kasturi R (eds) Document image analysis. IEEE Computer Society Press, Los Alamitos, pp 44–60
Valenti R, Sebe N, Gevers T (2009) Image saliency by isocentric curvedness and color. In: ICCV, IEEE computer society, pp 2185–2192. https://doi.org/10.1109/ICCV.2009.5459240
Walther D, Koch C (2006) Modeling attention to salient proto-objects. Neural Netw 19(9):1395–1407. https://doi.org/10.1016/j.neunet.2006.10.001
Wang H B, Lv H (2016) Salient object detection with fixation priori. In: 2016 international conference on machine learning and cybernetics (ICMLC), vol 1, pp 285–289. https://doi.org/10.1109/ICMLC.2016.7860915
Wang Z, Wu X (2016) Salient object detection using biogeography-based optimization to combine features. Appl Intell 1–17. https://doi.org/10.1007/s10489-015-0739-x
Wang W, Cai D, Xu X, Liew AWC (2014) Visual saliency detection based on region descriptors and prior knowledge. Signal Process Image Commun 29(3):424–433. https://doi.org/10.1016/j.image.2014.01.004
Wang H, Zhang P, Liu J (2015) Salient region detection by learning accurate background template. In: The 27th Chinese control and decision conference (2015 CCDC), pp 2519–2524. https://doi.org/10.1109/CCDC.2015.7162345
Wang Z, Jiang P, Wang F, Zhang X (2016) Recurrent double features: recurrent multi-scale deep features and saliency features for salient object detection. Springer International Publishing, Cham, pp 376–386
Weszka J S, Rosenfeld A (1977) Histogram modification for threshold selection. NASA STI/Recon Technical Report N 78
White J M, Rohrer G D (1983) Image thresholding for optical character recognition and other applications requiring character image extraction. IBM J Res Dev 27(4):400–411. https://doi.org/10.1147/rd.274.0400
Wu A Y, Hong T H, Rosenfeld A (1982) Threshold selection using quadtrees. IEEE Trans Pattern Anal Mach Intell PAMI-4(1):90–94. https://doi.org/10.1109/TPAMI.1982.4767203
Xiang D, Wang Z (2016) Salient object detection via saliency bias and diffusion. Multimed Tools Appl 1–20. https://doi.org/10.1007/s11042-016-3310-9
Xie Y, Lu H, Yang M H (2013) Bayesian saliency via low and mid level cues. IEEE Trans Image Process 22(5):1689–1698. https://doi.org/10.1109/TIP.2012.2216276
Xu K, Chen X (2013) A multi-stage area saliency detection model. In: 2013 4th IEEE international conference on software engineering and service science (ICSESS), pp 865–869. https://doi.org/10.1109/ICSESS.2013.6615442
Xu L, Zeng L, Duan H, Sowah N L (2014) Saliency detection in complex scenes. EURASIP J Image Vid Process 2014(1):1–13. https://doi.org/10.1186/1687-5281-2014-31
Xu X, Mu N, Zhang H, Fu X (2015) Salient object detection from distinctive features in low contrast images. In: 2015 IEEE international conference on image processing (ICIP), pp 3126–3130. https://doi.org/10.1109/ICIP.2015.7351379
Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 3166–3173. https://doi.org/10.1109/CVPR.2013.407
Yan X, Wang Y, Jiang M, Wang J (2014) Salient region detection via color spatial distribution determined global contrasts. In: 2014 IEEE international conference on image processing (ICIP), pp 1170–1174. https://doi.org/10.1109/ICIP.2014.7025233
Yang X, Qian X, Mei T (2015) Learning salient visual word for scalable mobile image retrieval. Pattern Recogn 48(10):3093–3101. https://doi.org/10.1016/j.patcog.2014.12.017. Discriminative feature learning from big data for visual recognition
Yasuda Y, Dubois M, Huang T S (1980) Data compression for check processing machines. Proc IEEE 68(7):874–885. https://doi.org/10.1109/PROC.1980.11753
Yeh M C, Hsu C F, Lu C J (2014) Fast salient object detection through efficient subwindow search. Pattern Recogn Lett 46:60–66. https://doi.org/10.1016/j.patrec.2014.05.006
Yu H, Li J, Tian Y, Huang T (2010) Automatic interesting object extraction from images using complementary saliency maps. In: Proceedings of the 18th ACM international conference on multimedia MM ’10, New York, pp 891–894, DOI https://doi.org/10.1145/1873951.1874105, (to appear in print)
Zhang Y J (1996) A survey on evaluation methods for image segmentation. Pattern Recogn 29(8):1335–1346. https://doi.org/10.1016/0031-3203(95)00169-7
Zhang D, Liu C (2014) A salient object detection framework beyond top-down and bottom-up mechanism. Biologically Insp Cogn Architect 9:1–8. https://doi.org/10.1016/j.bica.2014.06.005. Neural-symbolic networks for cognitive capacities
Zhang L, Yuan X (2015) Salient object detection with higher order potentials and learning affinity. IEEE Signal Processing Lett 22(9):1396–1399. https://doi.org/10.1109/LSP.2014.2377216
Zhang L, Tong M H, Marks T K, Cottrell G W (2008) SUN: a Bayesian framework for saliency using natural statistics. J Vis 8:1–20. https://doi.org/10.1167/8.7.32.Introduction
Zhang L, Gu Z, Li H (2013) Sdsp: a novel saliency detection method by combining simple priors. In: 2013 IEEE international conference on image processing, pp 171–175. https://doi.org/10.1109/ICIP.2013.6738036
Zhang J, Ehinger KA, Ding J, Yang J (2014) A prior-based graph for salient object detection. In: 2014 IEEE international conference on image processing (ICIP), pp 1175–1178. https://doi.org/10.1109/ICIP.2014.7025234 https://doi.org/10.1109/ICIP.2014.7025234
Zhang Y Y, Liu X Y, Wang H J (2014) Saliency detection via two-directional 2dpca analysis of image patches. Optik - Int J Light Electron Opt 125(24):7222–7226. https://doi.org/10.1016/j.ijleo.2014.07.132
Zhang M M, Li Z M, Bai H H, Sun Y (2014) Robust image salient regional extraction and matching based on dogss-msers. Optik - Int J Light Electron Opt 125(3):1469–1473. https://doi.org/10.1016/j.ijleo.2013.09.007
Zhang W, Xiong Q, Shi W, Chen S (2015) Region saliency detection via multi-feature on absorbing Markov chain. Vis Comput 32(3):275–287. https://doi.org/10.1007/s00371-015-1065-3
Zhang Q, Lin J, Li X (2016) Salient object detection via structure extraction and region contrast. J Inf Sci Eng 32:1435–1454
Zhang J, Sclaroff S, Lin Z, Shen X, Price B, Mech R (2016) Unconstrained salient object detection via proposal subset optimization. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 5733–5742
Zhang J, Ehinger KA, Wei H, Zhang K, Yang J (2017) A novel graph-based optimization framework for salient object detection. Pattern Recogn 64:39–50. https://doi.org/10.1016/j.patcog.2016.10.025
Zhang Q, Lin J, Tao Y, Li W, Shi Y (2017) Salient object detection via color and texture cues. Neurocomputing 243:35–48. https://doi.org/10.1016/j.neucom.2017.02.064
Zhao H, Chen J, Han Y, Cao X (2014) Image aesthetics enhancement using composition-based saliency detection. Multimed Syst 21(2):159–168. https://doi.org/10.1007/s00530-014-0373-1
Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1265–1274. https://doi.org/10.1109/CVPR.2015.7298731
Zhou L, Li YJ, Song YP, Qiao Y, Yang J (2014) Saliency driven clustering for salient object detection. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 5372–5376. https://doi.org/10.1109/ICASSP.2014.6854629
Zhou L, Yang Z, Chang G (2015) Salient region detection based on compactness with manifold ranking. In: 2015 5th international conference on information science and technology (ICIST), pp 108–112. https://doi.org/10.1109/ICIST.2015.7288950
Zhou L, Yang Z, Yuan Q, Zhou Z, Hu D (2015) Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans Image Process 24(11):3308–3320. https://doi.org/10.1109/TIP.2015.2438546
Zhou Q, Li N, Chen J, Cai S, Latecki LJ (2015) Salient object detection via background contrast. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1463–1467. https://doi.org/10.1109/ICASSP.2015.7178213
Zhu L, Klein D A, Frintrop S, Cao Z, Cremers A B (2014) A multisize superpixel approach for salient object detection based on multivariate normal distribution estimation. IEEE Trans Image Process 23(12):5094–5107. https://doi.org/10.1109/TIP.2014.2361024
Zou W, Kpalma K, Liu Z, Ronsin J (2013) Segmentation driven low-rank matrix recovery for saliency detection. In: British Machine vision conference, BMVC 2013. Bristol, pp 1–13. https://doi.org/10.5244/C.27.78
Zou B, Liu Q, Chen Z, Liu S, Zhang X (2015) Saliency detection using boundary information. Multimed Syst 22 (2):245–253. https://doi.org/10.1007/s00530-014-0449-y
Zou W, Liu Z, Kpalma K, Ronsin J, Zhao Y, Komodakis N (2015) Unsupervised joint salient region detection and object segmentation. IEEE Trans Image Process 24(11):3858–3873. https://doi.org/10.1109/TIP.2015.2456497
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Kumar, N. Thresholding in salient object detection: a survey. Multimed Tools Appl 77, 19139–19170 (2018). https://doi.org/10.1007/s11042-017-5329-y
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DOI: https://doi.org/10.1007/s11042-017-5329-y