Scene Categorization with Class Extendibility and Effective Discriminative Ability
Most of the numerous studies of scene categorization assume a fixed number of classes, and none categorize images with efficient class extendibility while preserving discriminative ability. This capability is crucial for an effective image categorization system. The proposed scene categorization method provides category-specific visual-word construction and image representation. The proposed method is effective for several reasons. First, since the visual-word construction and image representation are category-specific, image features related to the original classes need not be recreated when new classes are added, which minimizes reconstruction overhead. Second, since the visual-word construction and image representation are category-specific, the corresponding learning model for classification has substantial discriminating power. Experimental results confirm that the accuracy of the proposed method is superior to existing methods when using single-type and single-scale features.
KeywordsScene categorization classification category-specific class extendibility image retrieval visual words codebook
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- Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Van Gool, L.: Modeling scenes with local descriptors and latent aspects. In: Proc. IEEE Int. Conf. Computer Vision, pp. 883–890 (2005)Google Scholar
- Li, F.-F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, pp. 524–531 (2005)Google Scholar
- Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. IEEE Int. Conf. Computer Vision, pp. 2169–2178 (2006)Google Scholar
- Chang, C.-C., Lin, C.-J.: LIBSVM: A binary for support vector machine. Software available at, http://www.csie.ntu.edu.tw/cjlin/libsvm
- Dalal, N., Triggs, B.: Histogram of oriented gradient for human detection. In: Proc. Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar