SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean-Variance Analysis: A New Document Ranking Theory in Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Query Generation from Multiple Media Examples
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Similarity-based online feature selection in content-based image retrieval
IEEE Transactions on Image Processing
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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.