Abstract
The curse of dimensionality is a major issue in video indexing. Extremely high dimensional feature space seriously degrades the efficiency and the effectiveness of video retrieval. In this paper, we exploit the characteristics of document relevance and propose a statistical approach to learn an effective sub feature space from a multimedia document collection. This involves four steps: (1) density based feature term extraction, (2) factor analysis, (3) bi-clustering and (4) communality based component selection. Discrete feature terms are a set of feature clusters which smooth feature distribution in order to enhance the discrimination power; factor analysis tries to depict correlation between different feature dimensions in a loading matrix; bi-clustering groups both components and factors in the factor loading matrix and selects feature components from each bi-cluster according to the communality. We have conducted extensive comparative video retrieval experiments on the TRECVid 2006 collection. Significant performance improvements are shown over the baseline, PCA based K-mean clustering.
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References
Amati, G., Rijsbergen, C.J.V.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems (TOIS) 20(4), 357–389 (2002)
Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. PAMI 27(10), 1631–1643 (2005)
Harter, S.: A probabilistic approach to automatic keyword indexing, part i on the distribution of speciality words in a technical literature. Journal of the ASIS 26, 197–216 (1975)
Jiang, W., Er, G., Dai, Q., Gu, J.: Similarity-based online feature selection in content-based image retrieval. IEEE Transactions on Image Processing 15(3), 702–712 (2006)
Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)
Lin, T.-S., Meador, J.: Statistical feature extraction and selection for ic test pattern analysis. IEEE International Symposium on Circuits and Systems 1, 391–394 (1992)
Madeira, S., Oliveira, A.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1(1), 24–45 (2004)
Margulis, E.: N-poisson document modelling. In: SIGIR 1992, pp. 177–189. ACM Press, New York (1992)
Ren, R., Jose, J.M.: Query generation from multiple media examples. In: 7th International Workshop on Content-Based Multimedia Indexing, pp. 138–143 (2009)
Wang, J.: Mean-variance analysis: A new document ranking theory in information retrieval. In: Boughanem, M., et al. (eds.) ECIR 2009. LNCS, pp. 4–16. Springer, Heidelberg (2009)
Zhai, C., Lafferty, J.: A risk minimization framework for information retrieval. Inf. Process. Manage. 42(1), 31–55 (2006)
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Goyal, A., Ren, R., Jose, J.M. (2010). Feature Subspace Selection for Efficient Video Retrieval. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_76
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DOI: https://doi.org/10.1007/978-3-642-11301-7_76
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