Topic prerogative feature selection using multiple query examples for automatic video retrieval

Authors:
P. Punitha;Joemon M. Jose;Anuj Goyal
Affiliations:
University of Glasgow, 18 Lilybank Gardens, Glasgow, United Kingdom;University of Glasgow, 18 Lilybank Gardens, Glasgow, United Kingdom;University of Glasgow, 18 Lilybank Gardens, Glasgow, United Kingdom
Venue:
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Year:
2009

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Abstract

Well acceptance of relevance feedback and collaborative systems has given the users to express their preferences in terms of multiple query examples. The technology devised to utilize these user preferences, is expected to mine the semantic knowledge embedded within these query examples. In this paper, we propose a video mining framework based on dynamic learning from queries, using a statistical model for topic prerogative feature selection. The proposed method is specifically designed for multiple query example scenarios. The effectiveness of the proposed framework has been established with an extensive experimentation on TRECVid2007 data collection. The results reveal that our approach achieves a performance that is in par with the best results for this corpus without the requirement of any textual data.