Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11302
Title: Relevance feedback for real-world human action retrieval
Authors: Jones, S
Shao, L
Zhang, J
Liu, Y 
Keywords: Content-based video retrieval
Human action recognition
Relevance feedback
Issue Date: 2012
Publisher: North-Holland
Source: Pattern recognition letters, 2012, v. 33, no. 4, p. 446-452 How to cite?
Journal: Pattern recognition letters 
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.
URI: http://hdl.handle.net/10397/11302
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/j.patrec.2011.05.001
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