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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Keywords
- Principal Component Analysis
- Video Retrieval
- Multimedia Document
- Feature Subset Selection
- Document Representation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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
Publisher Name: Springer, Berlin, Heidelberg
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