Known-item video search via query-to-modality mapping
Kong-Wah Wan, Yan-Tao Zheng, Lekha Chaisorn · 2011
We introduce a novel query-to-modality mapping approach to the TRECVid 2010 known-Item video search (KIS) task. To search for a specific target video, a KIS query is verbose with many multi-modal attributes. Issuing all search terms to a retrieval engine will confuse the search criteria in different modalities and result in "topic drift". We propose decomposing a KIS query into a set of short uni-modal subqueries and issue them to the search index of the corresponding modality features, such as text-based metadata, visualbased high-level features. To do so, we introduce novel syntactic query features and cast the query-to-modality mapping as a classification problem. Retrieval results on the TRECVid 2010 KIS dataset shows that our approach outperforms existing methods by a significant margin.