Abstract
Image classification is a challenging problem in computer vision. Its performance heavily depends on image features extracted and classifiers to be constructed. In this paper, we present a new support vector machine with mixture of kernels (SVM-MK) for image classification. On the one hand, the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible. SVM-MK, on the other hand, is constructed to shoot for better classification performance. Experimental results on the Berg dataset show that the proposed image feature representation method together with the constructed image classifier, SVMMK, can achieve higher classification accuracy than conventional SVM with any single kernels as well as compare favorably with several state-of-the-art approaches.
Chapter PDF
Similar content being viewed by others
References
Chapelle, O., Haffner, P., Vapnik, V.: Support Vector Machines for Histogram-Based Image Classification. IEEE Transactions on Neural Networks 10(5), 1055–1064 (1999)
Goh, K.S., Chang, E., Cheng, K.T.: SVM Binary Classifier Ensembles for Image Classification. In: CIKM 2001, pp. 395–402 (2001)
Autio, I., Elomaa, T.: Flexible view recognition for indoor navigation based on Gabor filters and support vector machines. Pattern Recognition 36(12), 2769–2779 (2003)
Yang, C.B., Dong, M.: Region-based image annotation using asymmetrical support vector machine-based multiple instance learning. In: CVPR 2006, pp. 2057–2063 (2006)
Anthony, G., Gregg, H., Tshilidzi, M.: Image Classification Using SVMs: one-against- one Vs one-against-all. In: ACRS (2007)
Gao, K., Lin, S.X., Zhang, Y.D., et al.: Clustering guided SVM for semantic image retrieval. In: ICPCA 2007, pp. 199–203 (2007)
Jiang, Z.H., He, J., Guo, P.: Feature data optimization with LVQ technique in semantic image annotation. In: ISDA 2010, pp. 906–911 (2010)
Agrawal, S., Verma, N.K., Tamrakar, P., et al.: Content Based Color Image Classification using SVM. In: ITNG 2011, pp. 1090–1094 (2011)
Tsai, C.F., Lin, W.C.: A Comparative Study of Global and Local Feature Representations in Image Database Categorization. In: NCM 2009, pp. 1563–1566 (2009)
Chow, T.W.S., Rahman, M.K.M.: A new image classification technique using tree-structured regional features. Neurocomputing 70(4-6), 1040–1050 (2007)
Lu, H., Zheng, Y.B., Xue, X.Y., Zhang, Y.J.: Content and Context-Based Multi-Label Image Annotation. In: CVPRW 2009, pp. 61–68 (2009)
Shi, Z.P., Liu, X., Li, Q.Y., He, Q., Shi, Z.Z.: Extracting discriminative features for CBIR. Multimedia Tools and Applications (2011)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Zhu, Y.F., Tian, L.F., Mao, Z.Y., Wei, L.: Mixtures of Kernels for SVM Modeling. Springer, Heidelberg (2005)
Berg, T.L., Forsyth, D.A.: Animals on the web. In: CVPR 2006, pp. 1463–1470 (2006)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines (2001)
Schroff, F., Criminisi, A., Zisserman, A.: Harvesting image databases from the web. In: ICCV 2007, pp. 1–8 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 IFIP International Federation for Information Processing
About this paper
Cite this paper
Tian, D., Zhao, X., Shi, Z. (2012). Support Vector Machine with Mixture of Kernels for Image Classification. In: Shi, Z., Leake, D., Vadera, S. (eds) Intelligent Information Processing VI. IIP 2012. IFIP Advances in Information and Communication Technology, vol 385. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32891-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-32891-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32890-9
Online ISBN: 978-3-642-32891-6
eBook Packages: Computer ScienceComputer Science (R0)