Relevance feedback for real-world human action retrieval

Authors:
Simon Jones;Ling Shao;Jianguo Zhang;Yan Liu
Affiliations:
Department of Electronic & Electrical Engineering, The University of Sheffield, UK;Department of Electronic & Electrical Engineering, The University of Sheffield, UK;School of Computing, University of Dundee, UK;Department of Computing, Hong Kong Polytechnic University, Hong Kong
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

Content-based video retrieval is an increasingly popular research field, in large part due to the quickly growing catalogue of multimedia data to be found online. Even though a large portion of this data concerns humans, however, retrieval of human actions has received relatively little attention. Presented in this paper is a video retrieval system that can be used to perform a content-based query on a large database of videos very efficiently. Furthermore, it is shown that by using ABRS-SVM, a technique for incorporating Relevance feedback (RF) on the search results, it is possible to quickly achieve useful results even when dealing with very complex human action queries, such as in Hollywood movies.