Action retrieval with relevance feedback on YouTube videos

Jones, Simon and Shao, Ling (2011) Action retrieval with relevance feedback on YouTube videos. In: ICIMCS '11 - The Third International Conference on Internet Multimedia Computing and Service, 5th - 7th August 2011, Chengdu, China.

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Content-based retrieval systems are becoming increasingly relevant for managing large multimedia databases, such as those found on the Internet. In this paper, we investigate applying content-based retrieval with relevance feedback to the popular YouTube human action dataset, using a variety of methods to extract and compare features, in order to determine the most accurate techniques in this setting. Among other techniques, we explore soft-assignment code-book clustering, feature pruning, motion and static features, Adaboost and ABRS-SVM for relevance feedback. We evaluate the performance of several different systems to find the best combination of techniques for human action retrieval. We demonstrate that existing relevance feedback methods do not work well for YouTube media, and that a naive algorithm consistently outperforms these.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Action Recognition, Youtube Dataset, Feature Pruning, Soft-Assignment Clustering
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 16 Jun 2015 14:51
Last Modified: 13 Oct 2019 00:31

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