Skip to main content
SpringerLink
Log in

Perceptual Texture Similarity for Machine Intelligence Applications

  • Chapter
  • First Online:
Visual Content Indexing and Retrieval with Psycho-Visual Models

Part of the book series: Multimedia Systems and Applications ((MMSA))

  • 493 Accesses

Abstract

Textures are homogeneous visual phenomena commonly appearing in the visual scene. They are usually characterized by randomness with some stationarity. They have been well studied in different domains, such as neuroscience, vision science and computer vision, and showed an excellent performance in many applications for machine intelligence. This book chapter focuses on a special analysis task of textures for expressing texture similarity. This is quite a challenging task, because the similarity highly deviates from point-wise comparison. Texture similarity is key tool for many machine intelligence applications, such as recognition, classification, synthesis and etc. The chapter reviews the theories of texture perception, and provides a survey about the up-to-date approaches for both static and dynamic textures similarity. The chapter focuses also on the special application of texture similarity in image and video compression, providing the state of the art and prospects.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abraham, B., Camps, O.I., Sznaier, M.: Dynamic texture with fourier descriptors. In: Proceedings of the 4th International Workshop on Texture Analysis and Synthesis, pp. 53–58 (2005)

    Google Scholar 

  2. Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19(5), 1264–1274 (1989)

    Article  Google Scholar 

  3. Amiaz, T., Fazekas, S., Chetverikov, D., Kiryati, N.: Detecting regions of dynamic texture. In: Scale Space and Variational Methods in Computer Vision, pp. 848–859. Springer, Berlin (2007)

    Google Scholar 

  4. Bao, Z., Xu, C., Wang, C.: Perceptual auto-regressive texture synthesis for video coding. Multimedia Tools Appl. 64(3), 535–547 (2013)

    Article  Google Scholar 

  5. Ballé, J.: Subjective evaluation of texture similarity metrics for compression applications. In: Picture Coding Symposium (PCS), 2012, pp. 241–244. IEEE, New York (2012)

    Google Scholar 

  6. Barcelo, A., Montseny, E., Sobrevilla, P.: Fuzzy texture unit and fuzzy texture spectrum for texture characterization. Fuzzy Sets Syst. 158(3), 239–252 (2007)

    Article  MathSciNet  Google Scholar 

  7. Barmpoutis, P., Dimitropoulos, K., Grammalidis, N.: Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition. In: 2013 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), pp. 1078–1082. IEEE, New York (2014)

    Google Scholar 

  8. Beck, J.: Textural segmentation, second-order statistics, and textural elements. Biol. Cybern. 48(2), 125–130 (1983)

    Article  Google Scholar 

  9. Bosch, M., Zhu, F., Delp, E.J.: An overview of texture and motion based video coding at Purdue University. In: Picture Coding Symposium, 2009. PCS 2009, pp. 1–4. IEEE, New York (2009)

    Google Scholar 

  10. Bradley, D.C., Goyal, M.S.: Velocity computation in the primate visual system. Nature Rev. Neurosci. 9(9), 686–695 (2008)

    Article  Google Scholar 

  11. Caenen, G., Van Gool, L.: Maximum response filters for texture analysis. In: Conference on Computer Vision and Pattern Recognition Workshop, 2004. CVPRW’04, pp. 58–58. IEEE, New York (2004)

    Google Scholar 

  12. Campbell, N., Dalton, C., Gibson, D., Oziem, D., Thomas, B.: Practical generation of video textures using the auto-regressive process. Image Vis. Comput. 22(10), 819–827 (2004)

    Article  Google Scholar 

  13. Chang, W.-H., Yang, N.-C., Kuo, C.-M., Chen, Y.-J., et al.: An efficient temporal texture descriptor for video retrieval. In: Proceedings of the 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision, pp. 107–112. World Scientific and Engineering Academy and Society (WSEAS), Athens (2006)

    Google Scholar 

  14. Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W., Wld: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)

    Article  Google Scholar 

  15. Chessa, M., Sabatini, S.P., Solari, F.: A systematic analysis of a v1–mt neural model for motion estimation. Neurocomputing 173, 1811–1823 (2016)

    Article  Google Scholar 

  16. Chetverikov, D., Péteri, R.: A brief survey of dynamic texture description and recognition. In: Computer Recognition Systems, pp. 17–26. Springer, Berlin (2005)

    Google Scholar 

  17. Chubach, O., Garus, P., Wien, M.: Motion-based analysis and synthesis of dynamic textures. In: Proceedings of International Picture Coding Symposium PCS ’16, Nuremberg. IEEE, Piscataway (2016)

    Google Scholar 

  18. Costantini, R., Sbaiz, L., Süsstrunk, S.: Higher order SVD analysis for dynamic texture synthesis. IEEE Trans. Image Process. 17(1), 42–52 (2008)

    Article  MathSciNet  Google Scholar 

  19. Crivelli, T., Cernuschi-Frias, B., Bouthemy, P., Yao, J.-F.: Motion textures: modeling, classification, and segmentation using mixed-state Markov random fields. SIAM J. Image. Sci. 6(4), 2484–2520 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. David, S.V., Vinje, W.E., Gallant, J.L.: Natural stimulus statistics alter the receptive field structure of v1 neurons. J. Neurosci. 24(31), 6991–7006 (2004)

    Article  Google Scholar 

  21. Derpanis, K.G., Wildes, R.P.: Dynamic texture recognition based on distributions of spacetime oriented structure. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 191–198. IEEE, New York (2010)

    Google Scholar 

  22. Derpanis, K.G., Wildes, R.P.: Spacetime texture representation and recognition based on a spatiotemporal orientation analysis. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1193–1205 (2012)

    Article  Google Scholar 

  23. Derpanis, K.G., Sizintsev, M., Cannons, K.J., Wildes, R.P.: Action spotting and recognition based on a spatiotemporal orientation analysis. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 527–540 (2013)

    Article  Google Scholar 

  24. Dimitropoulos, K., Barmpoutis, P., Grammalidis, N.: Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans. Circ. Syst. Video Technol. 25(2), 339–351 (2015). doi:10.1109/TCSVT.2014.2339592

    Article  Google Scholar 

  25. Do, M.N., Vetterli, M.: Texture similarity measurement using Kullback-Leibler distance on wavelet subbands. In: 2000 International Conference on Image Processing, 2000. Proceedings, vol. 3, pp. 730–733. IEEE, New York (2000)

    Google Scholar 

  26. Do, M.N., Vetterli, M.: Wavelet-based texture retrieval using generalized gaussian density and Kullback-Leibler distance. IEEE Trans. Image Process. 11(2), 146–158 (2002)

    Article  MathSciNet  Google Scholar 

  27. Doretto, G., Soatto, S.: Editable dynamic textures. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings, pp. II–137, vol. 2. IEEE, New York (2003)

    Google Scholar 

  28. Doretto, G., Soatto, S.: Modeling dynamic scenes: an overview of dynamic textures. In: Handbook of Mathematical Models in Computer Vision, pp. 341–355. Springer, Berlin (2006)

    Google Scholar 

  29. Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. Int. J. Comput. Vis. 51(2), 91–109 (2003)

    Article  MATH  Google Scholar 

  30. Dubois, S., Péteri, R., Ménard, M.: A comparison of wavelet based spatio-temporal decomposition methods for dynamic texture recognition. In: Pattern Recognition and Image Analysis, pp. 314–321. Springer, Berlin (2009)

    Google Scholar 

  31. Dumitras, A., Haskell, B.G.: A texture replacement method at the encoder for bit-rate reduction of compressed video. IEEE Trans. Circuits Syst. Video Technol. 13(2), 163–175 (2003)

    Article  Google Scholar 

  32. Fan, G., Xia, X.-G.: Wavelet-based texture analysis and synthesis using hidden Markov models. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 50(1), 106–120 (2003)

    Google Scholar 

  33. Fazekas, S., Chetverikov, D.: Dynamic texture recognition using optical flow features and temporal periodicity. In: International Workshop on Content-Based Multimedia Indexing, 2007. CBMI’07, pp. 25–32. IEEE, New York (2007)

    Google Scholar 

  34. Fazekas, S., Amiaz, T., Chetverikov, D., Kiryati, N.: Dynamic texture detection based on motion analysis. Int. J. Comput. Vis. 82(1), 48–63 (2009)

    Article  Google Scholar 

  35. Ghadekar, P., Chopade, N.: Nonlinear dynamic texture analysis and synthesis model. Int. J. Recent Trends Eng. Technol. 11(2), 475–484 (2014)

    Google Scholar 

  36. Ghanem, B., Ahuja, N.: Maximum margin distance learning for dynamic texture recognition. In: European Conference on Computer Vision, pp. 223–236. Springer, Berlin (2010)

    Google Scholar 

  37. Goncalves, W.N., Bruno, O.M.: Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks. Expert Syst. Appl. 40(11), 4283–4300 (2013)

    Article  Google Scholar 

  38. Goncalves, W.N., Bruno, O.M.: Dynamic texture segmentation based on deterministic partially self-avoiding walks. Comput. Vis. Image Underst. 117(9), 1163–1174 (2013)

    Article  Google Scholar 

  39. Gonçalves, W.N., Machado, B.B., Bruno, O.M.: Spatiotemporal Gabor filters: a new method for dynamic texture recognition (2012). arXiv preprint arXiv:1201.3612

    Google Scholar 

  40. Grill-Spector, K., Malach, R.: The human visual cortex. Annu. Rev. Neurosci. 27, 649–677 (2004)

    Article  Google Scholar 

  41. Grossberg, S., Mingolla, E., Pack, C.: A neural model of motion processing and visual navigation by cortical area MST. Cereb. Cortex 9(8), 878–895 (1999)

    Article  Google Scholar 

  42. Guo, Y., Zhao, G., Zhou, Z., Pietikainen, M.: Video texture synthesis with multi-frame LBP-TOP and diffeomorphic growth model. IEEE Trans. Image Process. 22(10), 3879–3891 (2013)

    Article  MathSciNet  Google Scholar 

  43. Hadizadeh, H.: Visual saliency in video compression and transmission. Ph.D. Dissertation, Applied Sciences: School of Engineering Science (2013)

    Google Scholar 

  44. Hadizadeh, H., Bajic, I.V.: Saliency-aware video compression. IEEE Trans. Image Process. 23(1), 19–33 (2014)

    Article  MathSciNet  Google Scholar 

  45. Haindl, M., Filip, J.: Visual Texture: Accurate Material Appearance Measurement, Representation and Modeling. Springer Science & Business Media, London (2013)

    Book  Google Scholar 

  46. He, D.-C., Wang, L.: Texture unit, texture spectrum, and texture analysis. IEEE Trans. Geosci. Remote Sens. 28(4), 509–512 (1990)

    Article  Google Scholar 

  47. He, D.-C., Wang, L.: Simplified texture spectrum for texture analysis. J. Commun. Comput. 7(8), 44–53 (2010)

    Google Scholar 

  48. Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195(1), 215–243 (1968)

    Article  Google Scholar 

  49. Jin, G., Zhai, Y., Pappas, T.N., Neuhoff, D.L.: Matched-texture coding for structurally lossless compression. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 1065–1068. IEEE, New York (2012)

    Google Scholar 

  50. Joint Collaborative Team on Video Coding (JCT-VC) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG: High Efficiency Video Coding (HEVC) Test Model 16 (HM 16) Encoder Description. Technical Report (2014)

    Google Scholar 

  51. Julesz, B.: Visual pattern discrimination. IRE Trans. Inf. Theory 8(2), 84–92 (1962)

    Article  Google Scholar 

  52. Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290(5802), 91–97 (1981)

    Article  Google Scholar 

  53. Julész, B., Gilbert, E., Shepp, L., Frisch, H.: Inability of humans to discriminate between visual textures that agree in second-order statistics-revisited. Perception 2(4), 391–405 (1973)

    Article  Google Scholar 

  54. Julesz, B., Gilbert, E., Victor, J.D.: Visual discrimination of textures with identical third-order statistics. Biol. Cybern. 31(3), 137–140 (1978)

    Article  Google Scholar 

  55. Khandelia, A., Gorecha, S., Lall, B., Chaudhury, S., Mathur, M.: Parametric video compression scheme using ar based texture synthesis. In: Sixth Indian Conference on Computer Vision, Graphics & Image Processing, 2008. ICVGIP’08. IEEE, New York (2008), pp. 219–225

    Google Scholar 

  56. Kwatra, V., Essa, I., Bobick, A., Kwatra, N.: Texture optimization for example-based synthesis. In: ACM Transactions on Graphics (TOG), vol. 24(3), pp. 795–802. ACM, New York (2005)

    Google Scholar 

  57. Landy, M.S.: Texture Analysis and Perception. The New Visual Neurosciences, pp. 639–652. MIT, Cambridge (2013)

    Google Scholar 

  58. Landy, M.S., Graham, N.: Visual perception of texture. Vis. Neurosci. 2, 1106–1118 (2004)

    Google Scholar 

  59. Li, Y., Wang, T., Shum, H.-Y.: Motion texture: a two-level statistical model for character motion synthesis. In: ACM Transactions on Graphics (ToG), vol. 21(3), pp. 465–472. ACM, New York (2002)

    Google Scholar 

  60. Liu, M., Lu, L.: An improved rate control algorithm of h. 264/avc based on human visual system. In: Computer, Informatics, Cybernetics and Applications, pp. 1145–1151. Springer, Berlin (2012)

    Google Scholar 

  61. Liu, X., Wang, D.: A spectral histogram model for texton modeling and texture discrimination. Vis. Res. 42(23), 2617–2634 (2002)

    Article  Google Scholar 

  62. Liu, L., Fieguth, P., Guo, Y., Wang, X., Pietikäinen, M.: Local binary features for texture classification: taxonomy and experimental study. Pattern Recogn. 62, 135–160 (2017)

    Article  Google Scholar 

  63. Ma, C., Naser, K., Ricordel, V., Le Callet, P., Qing, C.: An adaptive lagrange multiplier determination method for dynamic texture in HEVC. In: IEEE International Conference on Consumer Electronics China. IEEE, New York (2016)

    Book  Google Scholar 

  64. Maggioni, M., Jin, G., Foi, A., Pappas, T.N.: Structural texture similarity metric based on intra-class variances. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 1992–1996. IEEE, New York (2014)

    Google Scholar 

  65. Malik, J., Perona, P.: Preattentive texture discrimination with early vision mechanisms. JOSA A 7(5), 923–932 (1990)

    Article  Google Scholar 

  66. Maloney, L.T., Yang, J.N.: Maximum likelihood difference scaling. J. Vis. 3(8), 5 (2003)

    Article  Google Scholar 

  67. Manjunath, B.S., Ma, W.-Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)

    Article  Google Scholar 

  68. Medathati, N.K., Chessa, M., Masson, G., Kornprobst, P., Solari, F.: Decoding mt motion response for optical flow estimation: an experimental evaluation. Ph.D. Dissertation, INRIA Sophia-Antipolis, France; University of Genoa, Genoa, Italy; INT la Timone, Marseille, France; INRIA (2015)

    Google Scholar 

  69. Montoya-Zegarra, J.A., Leite, N.J., da S Torres, R.: Rotation-invariant and scale-invariant steerable pyramid decomposition for texture image retrieval. In: SIBGRAPI 2007. XX Brazilian Symposium on Computer Graphics and Image Processing, 2007, pp. 121–128. IEEE, New York (2007)

    Google Scholar 

  70. Narain, R., Kwatra, V., Lee, H.-P., Kim, T., Carlson, M., Lin, M.C.: Feature-guided dynamic texture synthesis on continuous flows,. In: Proceedings of the 18th Eurographics conference on Rendering Techniques, pp. 361–370. Eurographics Association, Geneva (2007)

    Google Scholar 

  71. Naser, K., Ricordel, V., Le Callet, P.: Experimenting texture similarity metric STSIM for intra prediction mode selection and block partitioning in HEVC. In: 2014 19th International Conference on Digital Signal Processing (DSP), pp. 882–887. IEEE, New York (2014)

    Google Scholar 

  72. Naser, K., Ricordel, V., Le Callet, P.: Local texture synthesis: a static texture coding algorithm fully compatible with HEVC. In: 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 37–40. IEEE, New York (2015)

    Google Scholar 

  73. Naser, K., Ricordel, V., Le Callet, P.: Performance analysis of texture similarity metrics in HEVC intra prediction. In: Video Processing and Quality Metrics for Consumer Electronics (VPQM) (2015)

    Google Scholar 

  74. Naser, K., Ricordel, V., Le Callet, P.: Texture similarity metrics applied to HEVC intra prediction. In: The Third Sino-French Workshop on Information and Communication Technologies, SIFWICT 2015 (2015)

    Google Scholar 

  75. Naser, K., Ricordel, V., Le Callet, P.: A foveated short term distortion model for perceptually optimized dynamic textures compression in HEVC. In: 32nd Picture Coding Symposium (PCS). IEEE, New York (2016)

    Google Scholar 

  76. Naser, K., Ricordel, V., Le Callet, P.: Estimation of perceptual redundancies of HEVC encoded dynamic textures. In: 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–5. IEEE, New York (2016)

    Google Scholar 

  77. Naser, K., Ricordel, V., Le Callet, P.: Modeling the perceptual distortion of dynamic textures and its application in HEVC. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3787–3791. IEEE, New York (2016)

    Google Scholar 

  78. Ndjiki-Nya, P., Wiegand, T.: Video coding using texture analysis and synthesis. In: Proceedings of Picture Coding Symposium, Saint-Malo (2003)

    Google Scholar 

  79. Ndjiki-Nya, P., Makai, B., Blattermann, G., Smolic, A., Schwarz, H., Wiegand, T.: Improved h. 264/avc coding using texture analysis and synthesis. In: 2003 International Conference on Image Processing, 2003. ICIP 2003. Proceedings, vol. 3, pp. III–849. IEEE, New York (2003)

    Google Scholar 

  80. Ndjiki-Nya, P., Hinz, T., Smolic, A., Wiegand, T.: A generic and automatic content-based approach for improved h. 264/mpeg4-avc video coding. In: IEEE International Conference on Image Processing, 2005. ICIP 2005, vol. 2, pp. II–874. IEEE, New York (2005)

    Google Scholar 

  81. Ndjiki-Nya, P., Bull, D., Wiegand, T.: Perception-oriented video coding based on texture analysis and synthesis. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 2273–2276. IEEE, New York (2009)

    Google Scholar 

  82. Nelson, R.C., Polana, R.: Qualitative recognition of motion using temporal texture. CVGIP: Image Underst. 56(1), 78–89 (1992)

    Article  MATH  Google Scholar 

  83. Nishimoto, S., Gallant, J.L.: A three-dimensional spatiotemporal receptive field model explains responses of area mt neurons to naturalistic movies. J. Neurosci. 31(41), 14551–14564 (2011)

    Article  Google Scholar 

  84. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  85. Ontrup, J., Wersing, H., Ritter, H.: A computational feature binding model of human texture perception. Cogn. Process. 5(1), 31–44 (2004)

    Article  Google Scholar 

  86. Oxford Dictionaries. [Online]. Available: http://www.oxforddictionaries.com

  87. Pack, C., Grossberg, S., Mingolla, E.: A neural model of smooth pursuit control and motion perception by cortical area MST. J. Cogn. Neurosci. 13(1), 102–120 (2001)

    Article  Google Scholar 

  88. Pappas, T.N., Neuhoff, D.L., de Ridder, H., Zujovic, J.: Image analysis: focus on texture similarity. Proc. IEEE 101(9), 2044–2057 (2013)

    Article  Google Scholar 

  89. Peh, C.-H., Cheong, L.-F.: Synergizing spatial and temporal texture. IEEE Trans. Image Process. 11(10), 1179–1191 (2002)

    Article  MathSciNet  Google Scholar 

  90. Perrone, J.A.: A visual motion sensor based on the properties of v1 and mt neurons. Vision Res. 44(15), 1733–1755 (2004)

    Article  Google Scholar 

  91. Perry, C.J., Fallah, M.: Feature integration and object representations along the dorsal stream visual hierarchy. Front. Comput. Neurosci. 8, 84 (2014)

    Article  Google Scholar 

  92. Péteri, R., Chetverikov, D.: Dynamic texture recognition using normal flow and texture regularity. In: Pattern Recognition and Image Analysis, pp. 223–230. Springer, Berlin (2005)

    Google Scholar 

  93. Péteri, R., Fazekas, S., Huiskes, M.J.: Dyntex: a comprehensive database of dynamic textures. Pattern Recogn. Lett. 31(12), 1627–1632 (2010)

    Article  Google Scholar 

  94. Pollen, D.A., Ronner, S.F.: Visual cortical neurons as localized spatial frequency filters. IEEE Trans. Syst. Man Cybern. SMC-13(5), 907–916 (1983)

    Article  Google Scholar 

  95. Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40(1), 49–70 (2000)

    Article  MATH  Google Scholar 

  96. Rahman, A., Murshed, M.: Real-time temporal texture characterisation using block-based motion co-occurrence statistics. In: International Conference on Image Processing (2004)

    Book  Google Scholar 

  97. Rahman, A., Murshed, M.: A motion-based approach for temporal texture synthesis. In: TENCON 2005 IEEE Region 10, pp. 1–4. IEEE, New York (2005)

    Google Scholar 

  98. Rosenholtz, R.: Texture Perception. Oxford Handbooks Online (2014)

    Google Scholar 

  99. Rust, N.C., Mante, V., Simoncelli, E.P., Movshon, J.A.: How mt cells analyze the motion of visual patterns. Nature Neurosci. 9(11), 1421–1431 (2006)

    Article  Google Scholar 

  100. Saisan, P., Doretto, G., Wu, Y.N., Soatto, S.: Dynamic texture recognition. In: CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, vol. 2, pp. II–58. IEEE, New York (2001)

    Google Scholar 

  101. Simoncelli, E.P., Freeman, W.T., Adelson, E.H., Heeger, D.J.: Shiftable multiscale transforms. IEEE Trans. Inf. Theory 38(2), 587–607 (1992)

    Article  MathSciNet  Google Scholar 

  102. Simoncelli, E.P., Heeger, D.J.: A model of neuronal responses in visual area mt. Vis. Res. 38(5), 743–761 (1998)

    Article  Google Scholar 

  103. Smith, J.R., Lin, C.-Y., Naphade, M., Video texture indexing using spatio-temporal wavelets. In: 2002 International Conference on Image Processing. 2002. Proceedings, vol. 2, pp. II–437. IEEE, New York (2002)

    Google Scholar 

  104. Soatto, S., Doretto, G., and Wu, Y.N., Dynamic textures. In: Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001. Proceedings, vol. 2, pp. 439–446. IEEE, New York (2001)

    Google Scholar 

  105. Solari, F., Chessa, M., Medathati, N.K., Kornprobst, P.: What can we expect from a v1-mt feedforward architecture for optical flow estimation? Signal Process. Image Commun. 39, 342–354 (2015)

    Article  Google Scholar 

  106. Sullivan, G.J., Ohm, J., Han, W.-J., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012)

    Article  Google Scholar 

  107. Sun, C., Wang, H.-J., Li, H., Kim, T.-H.: Perceptually adaptive Lagrange multiplier for rate-distortion optimization in h. 264. In: Future Generation Communication and Networking (FGCN 2007), vol. 1, pp. 459–463. IEEE, New York (2007)

    Google Scholar 

  108. Sun, X., Yin, B., Shi, Y.: A low cost video coding scheme using texture synthesis. In: 2nd International Congress on Image and Signal Processing, 2009. CISP’09, pp. 1–5. IEEE, New York (2009)

    Google Scholar 

  109. Swamy, D.S., Butler, K.J., Chandler, D.M., Hemami, S.S.: Parametric quality assessment of synthesized textures. In: Proceedings of Human Vision and Electronic Imaging (2011)

    Google Scholar 

  110. Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–473 (1978)

    Article  Google Scholar 

  111. Thakur, U.S., Ray, B.: Image coding using parametric texture synthesis. In: 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2016)

    Google Scholar 

  112. Thakur, U., Naser, K., Wien, M.: Dynamic texture synthesis using linear phase shift interpolation. In: Proceedings of International Picture Coding Symposium PCS ’16, Nuremberg. IEEE, Piscataway (2016)

    Google Scholar 

  113. Tiwari, D., Tyagi, V.: Dynamic texture recognition based on completed volume local binary pattern. Multidim. Syst. Sign. Process. 27(2), 563–575 (2016)

    Article  Google Scholar 

  114. Tiwari, D., Tyagi, V.: Dynamic texture recognition using multiresolution edge-weighted local structure pattern. Comput. Electr. Eng. 11, 475–484 (2016)

    Google Scholar 

  115. Tiwari, D., Tyagi, V.: Improved weber’s law based local binary pattern for dynamic texture recognition. Multimedia Tools Appl. 76, 1–18 (2016)

    Google Scholar 

  116. Tlapale, E., Kornprobst, P., Masson, G.S., Faugeras, O.: A neural field model for motion estimation. In: Mathematical image processing, pp. 159–179. Springer, Berlin (2011)

    Google Scholar 

  117. Tuceryan, M., Jain, A.K.: Texture Analysis. The Handbook of Pattern Recognition and Computer Vision, vol. 2, pp. 207–248 (1998)

    Google Scholar 

  118. Turner, M.R.: Texture discrimination by Gabor functions. Biol. Cybern. 55(2–3), 71–82 (1986)

    Google Scholar 

  119. Valaeys, S., Menegaz, G., Ziliani, F., Reichel, J.: Modeling of 2d+ 1 texture movies for video coding. Image Vis. Comput. 21(1), 49–59 (2003)

    Article  Google Scholar 

  120. van der Maaten, L., Postma, E.: Texton-based texture classification. In: Proceedings of Belgium-Netherlands Artificial Intelligence Conference (2007)

    Google Scholar 

  121. Varadarajan, S., Karam, L.J.: Adaptive texture synthesis based on perceived texture regularity. In: 2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX), pp. 76–80. IEEE, New York (2014)

    Google Scholar 

  122. Wang, Y., Zhu, S.-C.: Modeling textured motion: particle, wave and sketch. In: Ninth IEEE International Conference on Computer Vision, 2003. Proceedings, pp. 213–220. IEEE, New York (2003)

    Google Scholar 

  123. Wang, L., Liu, H., Sun, F.: Dynamic texture classification using local fuzzy coding. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1559–1565. IEEE, New York (2014)

    Google Scholar 

  124. Wei, L.-Y., Lefebvre, S., Kwatra, V., Turk, G.: State of the art in example-based texture synthesis. In: Eurographics 2009, State of the Art Report, EG-STAR, pp. 93–117. Eurographics Association, Geneva (2009)

    Google Scholar 

  125. Wong, C.-W., Au, O.C., Meng, B., Lam, K.: Perceptual rate control for low-delay video communications. In: 2003 International Conference on Multimedia and Expo, 2003. ICME’03. Proceedings, vol. 3, pp. III–361. IEEE, New York (2003)

    Google Scholar 

  126. Xu, Y., Quan, Y., Ling, H., Ji, H.: Dynamic texture classification using dynamic fractal analysis. In: 2011 International Conference on Computer Vision, pp. 1219–1226. IEEE, New York (2011)

    Google Scholar 

  127. Xu, Y., Huang, S., Ji, H., Fermüller, C.: Scale-space texture description on sift-like textons. Comput. Vis. Image Underst. 116(9), 999–1013 (2012)

    Article  Google Scholar 

  128. Xu, Y., Quan, Y., Zhang, Z., Ling, H., Ji, H.: Classifying dynamic textures via spatiotemporal fractal analysis. Pattern Recogn. 48(10), 3239–3248 (2015)

    Article  Google Scholar 

  129. Xu, L., et al.: Free-energy principle inspired video quality metric and its use in video coding. IEEE Trans. Multimedia 18(4), 590–602 (2016)

    Article  Google Scholar 

  130. Yu, H., Pan, F., Lin, Z., Sun, Y.: A perceptual bit allocation scheme for h. 264. In: IEEE International Conference on Multimedia and Expo, 2005. ICME 2005, p. 4. IEEE, New York (2005)

    Google Scholar 

  131. Yuan, L., Wen, F., Liu, C., Shum, H.-Y.: Synthesizing dynamic texture with closed-loop linear dynamic system. In: Computer Vision-ECCV 2004, pp. 603–616. Springer, Berlin (2004)

    Google Scholar 

  132. Zhai, Y., Neuhoff, D.L.: Rotation-invariant local radius index: a compact texture similarity feature for classification. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5711–5715. IEEE, New York (2014)

    Google Scholar 

  133. Zhai, Y., Neuhoff, D.L., Pappas, T.N.: Local radius index-a new texture similarity feature. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1434–1438. IEEE, New York (2013)

    Google Scholar 

  134. Zhang, F., Bull, D.R.: A parametric framework for video compression using region-based texture models. IEEE J. Sel. Top. Sign. Proces. 5(7), 1378–1392 (2011)

    Article  Google Scholar 

  135. Zhang, J., Tan, T.: Brief review of invariant texture analysis methods. Pattern Recogn. 35(3), 735–747 (2002)

    Article  MATH  Google Scholar 

  136. Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)

    Article  Google Scholar 

  137. Zhao, X., Reyes, M.G., Pappas, T.N., Neuhoff, D.L.: Structural texture similarity metrics for retrieval applications. In: 15th IEEE International Conference on Image Processing, 2008. ICIP 2008, pp. 1196–1199. IEEE, New York (2008)

    Google Scholar 

  138. Zujovic, J., Pappas, T.N., Neuhoff, D.L.: Structural similarity metrics for texture analysis and retrieval. In: 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE, New York (2009)

    Google Scholar 

  139. Zujovic, J., Pappas, T.N., Neuhoff, D.L., van Egmond, R., de Ridder, H.: Subjective and objective texture similarity for image compression. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1369–1372. IEEE, New York (2012)

    Google Scholar 

  140. Zujovic, J., Pappas, T.N., Neuhoff, D.L.: Structural texture similarity metrics for image analysis and retrieval. IEEE Trans. Image Process. 22(7), 2545–2558 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Marie Sklodowska-Curie under the PROVISION (PeRceptually Optimized VIdeo CompresSION) project bearing Grant Number 608231 and Call Identifier: FP7-PEOPLE-2013-ITN.

Author information

Authors and Affiliations

  1. LS2N, UMR CNRS 6004, Polytech Nantes, University of Nantes, Rue Christian Pauc, BP 50609, 44306, Nantes Cedex 3, France

    Karam Naser & Vincent Ricordel

  2. LS2N UMR CNRS 6004, Université de Nantes, Nantes Cedex 3, France

    Patrick Le Callet

Authors
  1. Karam Naser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent Ricordel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick Le Callet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karam Naser .

Editor information

Editors and Affiliations

  1. LaBRI UMR 5800, Univ. Bordeaux, CNRS, Bordeaux INP, Univ. Bordeaux, Talence, France

    Jenny Benois-Pineau

  2. LS2N, UMR CNRS 6004, Université de Nantes, Nantes Cedex 3, France

    Patrick Le Callet

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Naser, K., Ricordel, V., Le Callet, P. (2017). Perceptual Texture Similarity for Machine Intelligence Applications. In: Benois-Pineau, J., Le Callet, P. (eds) Visual Content Indexing and Retrieval with Psycho-Visual Models. Multimedia Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-57687-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57687-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57686-2

  • Online ISBN: 978-3-319-57687-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation