Relevance feedback for real-world human action retrieval

Jones, Simon, Shao, Ling, Zhang, Jianguo and Liu, Yan (2012) Relevance feedback for real-world human action retrieval. Pattern Recognition Letters, 33 (4). pp. 446-452. ISSN 0167-8655

Full text not available from this repository. (Request a copy)
Official URL:


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.

Item Type: Article
Uncontrolled Keywords: Content-based video retrieval; Relevance feedback; Human action recognition
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 15:19
Last Modified: 12 Oct 2019 22:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics