Abstract
Content-based image retrieval (CBIR) is a widely used technique for retrieval images from huge and unlabeled image databases. However, users are not satisfied with the traditional information retrieval techniques. Moreover, the emergence of web development and transmission networks and also the amount of images which are available to users continue to grow. Therefore, a permanent and considerable digital image production in many areas takes place. Hence, the rapid access to these huge collections of images and retrieve similar image of a given image (Query) from this large collection of images presents major challenges and requires efficient techniques. The performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurement. For this reason, we present, on this paper, a simple but effective deep learning framework based on Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for fast image retrieval composed of feature extraction and classification. From several extensive of empirical studies for a variety of CBIR tasks using image database, we obtain some encouraging results which reveals several important insights for improving the CBIR performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lecun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Wan, J., Wang, D., Hoi, S., Steven, C.H., et al.: Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 157–166. ACM (2014)
Piras, L., Giacinto, G.: Information fusion in content based image retrieval: a comprehensive overview. Inf. Fusion 37, 50–60 (2017)
Ren, J.: Investigation of convolutional neural network architectures for image-based feature learning and classification. Thesis (2017)
Wang, H., Cai, Y., Zhang, Y., Pan, H., Lv, W., Han, H.: Deep learning for image retrieval: what works and what doesn’t. In: IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1576–1583 (2015)
Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 27–35 (2015)
Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: Proceedings of the Ninth ACM International Conference on Multimedia, pp. 107–118. ACM (2001)
Fu, R., Li, B., Gao, Y., Wang, P.: Content-based image retrieval based on CNN and SVM. In: 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 638–642. IEEE (2016)
Niu, X.X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognit. 45(4), 1318–1325 (2012)
Tang, Y.: Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 (2013)
Paola, J.D., Schowengerdt, R.A.: Review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. Int. J. Remote Sensing 16(16), 3033–3058 (1995)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Tang, Y.: Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 (2013)
Niu, X.X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognit. 45(4), 1318–1325 (2012)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001). http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chen, Y., Jiang, H., Li, C., Jia, X., et al.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sensing 54(10), 6232–6251 (2016)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: Advances in Neural Information Processing Systems, pp. 507–513 (1998)
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Foundations and trends. Comput. Graph. Vis. 3(3), 177–280 (2008)
Acknowledgment
We want to thank all the researchers for their previous work. In the development of our algorithm, Libsvm [17] is utilized.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Mohamed, O., Khalid, E.A., Mohammed, O., Brahim, A. (2019). Content-Based Image Retrieval Using Convolutional Neural Networks. In: Mizera-Pietraszko, J., Pichappan, P., Mohamed, L. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2017. Advances in Intelligent Systems and Computing, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-91337-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-91337-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91336-0
Online ISBN: 978-3-319-91337-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)