MSRA atT TRECVID 2008: High-Level Feature Extraction and Automatic Search

This paper describes the MSRA experiments for TRECVID 2008. We performed the experiments in high-level feature extraction and automatic search tasks. For high-level feature extraction, we representatively investigated the benefit of global and local low-level features by a variety of learning-based methods, including supervised and semi-supervised learning algorithms. For automatic search, we focused on text and visual baseline, query-independent learning, and various reranking methods.

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