Human Action Retrieval via efficient feature matching

Tang, Jun, Shao, Ling and Zhen, Xiantong (2013) Human Action Retrieval via efficient feature matching. In: AVSS 2013 - 10th International Conference on Advanced Video and Signal Based Surveillance, 27th - 30th August 2013, Krakow, Poland.

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As a large proportion of the available video media concerns humans, human action retrieval is posed as a new topic in the domain of content-based video retrieval. For retrieving complex human actions, measuring the similarity between two videos represented by local features is a critical issue. In this paper, a fast and explicit feature correspondence approach is presented to compute the match cost serving as the similarity metric. Then the proposed similarity metric is embedded into the framework of manifold ranking for action retrieval. In contrast to the Bag-of-Words model and its variants, our method yields an encouraging improvement of accuracy on the KTH and the UCF YouTube datasets with reasonably efficient computation.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: content-based retrieval, feature extraction, image matching, image representation, video retrieval
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 16 Jun 2015 12:11
Last Modified: 13 Oct 2019 00:33

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