Abstract
The Bag-of-Words (BoW) derived from local keypoints was widely applied in visual information research such as image search, video retrieval, object categorization, and computer vision. Construction of visual codebook is a well-known and predominant method for the representation of BoW. However, a visual codebook usually has a high dimension that results in high computational complexity. In this paper, an approach is presented for constructing a compact visual codebook. Two important parameters, namely the likelihood ratio and the significant level, are proposed to estimate the discriminative capability of each of the codewords. Thus, the codewords that have higher discriminative capability are reserved, and the others are removed. Experiments prove that application of the proposed compact codebook not only reduces computational complexity, but also improves performance of object classification..
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References
Jiang, Y., Ngo, C., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: CIVR (2007)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: CVPR (2003)
Lowe, D.: Distinctive image features from scale invariant keypoints. IJCV 60(2), 91–110 (2004)
Li, F.F., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: CVPR Workshop on Generative-Model Based Vision (2004)
Kim, S., Kweon, I.S., Lee, C.W.: Visual Categorization Robust to Large Intra-Class Variations using Entropy-guided Codebook. In: IEEE International Conference on Robotics and Automation Roma, Italy, April 10-14 (2007)
Wu, L.N., Luo, S.W., Sun, W.: Create efficient visual codebook based on weighted mRMR for object categorization. In: ICSP 9th International Conference (2008)
Hotta, K.: Object Categorization Based on Kernel Principal Component Analysis of Visual Words. In: WACV (2008)
Chang, S.F., He, J.F., et al.: Columbia University/VIREO-CityU/IRIT TRECVID2008 High-Level Feature Extraction and Interactive Video Search. In: Proc. TRECVID 2008 (2008)
Snoek, C.G.M., Van, K.E.A., et al.: The MediaMill TRECVID 2008 Semantic Video Search Engine. In: Proc. TRECVID 2008 (2008)
Wang, L.: Toward a discriminative codebook: codeword selection across multi-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, June 17-22, pp. 1–8 (2007)
Li, T., Mei, T., Kweon, I.S.: Learning Optimal Compact Codebook for Efficient Object Categorization. In: WACV (2008)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/cjlin/libsvm
TREC Video Retrieval Evaluation (TRECVID), http://www-nlpir.nist.gov/projects/trecvid/
Everingham, M., Zisserman, A., et al.: The 2005 pascal visual object classes challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 117–176. Springer, Heidelberg (2006)
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Wang, Z., Liu, G., Qian, X., Guo, D. (2010). An Approach to the Compact and Efficient Visual Codebook Based on SIFT Descriptor. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_42
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DOI: https://doi.org/10.1007/978-3-642-15702-8_42
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