Relevance feedback for real-world human action retrieval
نویسندگان
چکیده
0167-8655/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.patrec.2011.05.001 ⇑ Corresponding author. E-mail address: [email protected] (L. Shao) 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. 2011 Elsevier B.V. All rights reserved.
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ورودعنوان ژورنال:
- Pattern Recognition Letters
دوره 33 شماره
صفحات -
تاریخ انتشار 2012