Abstract
In this paper, we propose an efficient sparse feature on-line learning approach for image classification. A large-margin formulation solved by linear programming is adopted to learn sparse features on the max-similarity based image representation. The margins between the training images and the query images can be directly utilized for classification by the Naive-Bayes or the K Nearest Neighbor category classifier. Balancing between efficiency and classification accuracy is the most attractive characteristic of our approach. Efficiency lies in its on-line sparsity learning algorithm and direct usage of margins, while accuracy depends on the discriminative power of selected sparse features with their weights. We test our approach using much fewer features on Caltech-101 and Scene-15 datasets and our classification results are comparable to the state-of-the-art.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Rosch, E.: Natural categories. Cognitive Psychology 4, 328–350 (1973)
Caetano, T., Cheng, L., Le, Q., Smola, A.: Learning graph matching. In: ICCV 2007 (2007)
Torresani, L., Kolmogorov, V., Rother, C.: Feature correspondence via graph matching: Models and global optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008)
Chen, L., McAuley, J., Feris, R., Caetano, T., Turk, M.: Shape classification through structured learning of matching measures. In: CVPR 2009, pp. 365–372 (2009)
Zhang, Z., Li, Z.N., Drew, M.S.: Learning image similarities via probabilistic feature matching. In: ICIP, pp. 1857–1860 (2010)
Zhang, H., Berg, A., Maire, M., Malik, J.: Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: CVPR 2006, vol. II, pp. 2126–2136 (2006)
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: CVPR 2008, pp. 1–8 (2008)
Yuan, J., Liu, Z., Wu, Y.: Discriminative subvolume search for efficient action detection. In: CVPR 2009, pp. 2442–2449 (2009)
Frome, A., Singer, Y., Malik, J.: Image retrieval and classification using local distance functions. In: NIPS 2006, pp. 417–424. MIT Press, Cambridge (2006)
Frome, A., Singer, Y., Sha, F., Malik, J.: Learning globally-consistent local distance functions for shape-based image retrieval and classification. In: ICCV 2007, pp. 1–8 (2007)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Torresani, L., Szummer, M., Fitzgibbon, A.: Learning query-dependent prefilters for scalable image retrieval. In: CVPR 2009, pp. 2615–2622 (2009)
Gehler, P.V., Nowozin, S.: On feature combination for multiclass object classification. In: ICCV 2009, pp. 1–8 (2009)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR 2006 (2006)
Bi, J., Chen, Y., Wang, J.: A sparse support vector machine approach to region-based image categorization. In: CVPR 2005, pp. 1121–1128 (2005)
Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: ICCV 2009, pp. 1–8 (2009)
Fung, G.M., Mangasarian, O.L.: A feature selection newton method for support vector machine classification. Comput. Optim. Appl. 28(2), 185–202 (2004)
Berg, A., Malik, J.: Geometric blur and template matching. In: CVPR 2001, pp. 607–614 (2001)
Ramanan, D., Baker, S.: Local distance functions: A taxonomy, new algorithms, and an evaluation. In: ICCV 2009 (2009)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. PAMI (2010) (in press)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/discriminative approach. PAMI 30(4), 712–727 (2008)
Liu, J., Shah, M.: Scene modeling using co-clustering. In: ICCV 2007, pp. 1–7 (2007)
Rasiwasia, N., Vasconcelos, N.: Scene classification with low-dimensional semantic spaces and weak supervision. In: CVPR 2008, pp. 1–6 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Z., Huang, J., Li, ZN. (2011). Learning Sparse Features On-Line for Image Classification. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-21593-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21592-6
Online ISBN: 978-3-642-21593-3
eBook Packages: Computer ScienceComputer Science (R0)