Video content modeling with latent semantic analysis
Fabrice Souvannavong · 2003
In this paper we present a novel approach to fully automatic video content modeling. We introduce the concept of visual dictionary to describe visual video elements, called words, which appear through video sequences. Their cooccurrences in contexts, i.e. the main video entity to be indexed (frame, shot, scene, ¡ ¢ ), compose signatures usable for indexing and comparison. Latent Semantic Analysis (LSA) is naturally introduced to improve the robustness to noise and discover the latent semantic. This new representation along with its associated similarity measure, has many applications including indexing, retrieval, summarization or enhanced navigation, on single as well as multiple video sequences. Once the framework is presented, we investigate three methods to efficiently exploit the information provided by multiple features in order to improve the video analysis. Promising results were obtained on the object and frame retrieval tasks across a single video document. 1.