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Relevance feedback for real-world human action retrieval

Relevance feedback for real-world human action retrieval

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Original languageEnglish
Pages446-452
Number of pages7
JournalPattern Recognition Letters
Journal publication dateMar 2012
Journal number4
Volume33
DOIs
StatePublished

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. (C) 2011 Elsevier B.V. All rights reserved.

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