Abstract
For a support vector machine (SVM) classifier applied to image annotation, if too many training samples are used, the training speed might be very slow and also bring the problem of declining the classification accuracy. Learning vector quantization (LVQ) technique provides a framework to select some representative vectors which can be used to train the classifier instead of using original training data. A novel method which combines affinity propagation algorithm based LVQ technique and SVM classifier is proposed to annotate images. Experimental results demonstrate that proposed method has a better speed performance than that of SVM without applying LVQ.
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References
Perronnin, F.: Universal and Adapted Vocabularies for Generic Visual Categorization. IEEE Trans. PAMI 30(7), 1243–1256 (2008)
Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised Learning of Semantic Classes for Image Annotation and Retrieval. IEEE Trans. PAMI 29(3), 394–410 (2007)
Cusano, C., Ciocca, G., Schettini, R.: Image annotation using SVM. In: Proceedings of SPIE, vol. 5304, pp. 330–338 (2004)
Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. J. Machine Learning Research 5, 913–939 (2004)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Shao, W.B., Phung, S.L., Naghdy, G.: A multi-class image classification system using salient features and support vector machines. In: Proceedings of International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 431–436 (2007)
Kohenen, T.: Self-Organizing Maps. Springer, Berlin (2001)
Chang, C.Y., Wang, H.J., Fu, S.Y.: Texture Image Classification Using Modular Radial Basis Function Neural Networks. J. of Electronic Image 19(1), 1–11 (2010)
Viitaniemi, V., Laaksonen, J.: Evaluating the performance in automatic image annotation: Example case by adaptive fusion of global image features. Image Comm. 22(6), 557–568 (2007)
Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Zhang, H.J.: Image Classification for Content-Based Indexing. IEEE Trans. Image Processing 10(1), 117–130 (2001)
Jiang, Z., He, J., Guo, P.: Feature Data Optimization with LVQ Technique in Semantic Image Annotation, accepted to ISDA 2010 (2010)
Frey, B.J., Dueck, D.: Clustering by Passing Messages between Data Points. Science 315, 972–976 (2007)
Dueck, D., Frey, B.J.: Non-metric Affinity Propagation for Unsupervised Image Categorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8. IEEE Press, Brazil (2007)
Yang, D., Guo, P.: Image Modeling with Combined Optimization Techniques for Image Semantic Annotation. Neural Computing and Applications (in press, 2010)
Stanford vision lab, http://vision.stanford.edu/resources_links.html
Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. on Sys. Man and Cyb. 3(6), 610–621 (1973)
Tamura, H., Mori, S., Yamawaki, T.: Texture features corresponding to visual perception. IEEE Trans. on Sys., Man and Cyb. 8(6), 460–473 (1978)
LIBSVM-A, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Guo, P., Jia, Y.D., Lyu, M.R.: A Study of Regularized Gaussian Classifier in High-Dimension Small Sample Set Case Based on MDL Principle with Application to Spectrum Recognition. Patt. Recog. 41(9), 2842–2854 (2008)
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Lin, S., Yao, Y., Guo, P. (2010). Speed Up Image Annotation Based on LVQ Technique with Affinity Propagation Algorithm. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_66
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DOI: https://doi.org/10.1007/978-3-642-17534-3_66
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