A pseudo relevance feedback based cross domain video concept detection

Authors:
Xu Shaoxi;Yang Jing;Tang Sheng;Zhang Yong-Dong
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, China;Hunan University of Technology, China;Institute of Computing Technology, Chinese Academy of Sciences, China;Institute of Computing Technology, Chinese Academy of Sciences, China
Venue:
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Year:
2011

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Abstract

Due to the mismatch of data distribution between training and testing data set, the issue of semantic gap in the field of video concept detection becomes more and more serious. To solve this problem, an effective pseudo relevance feedback (PRF) based method is proposed in this paper to build domain adaptive classifiers. Firstly, the mechanism of PRF tries to select some pseudo samples according to the fused estimation for test data given by existing source models. Then, these pseudo samples are integrated into the process of Tradboost based cross domain transfer learning to make the best use of semantic information generalized by existing source models. Extensive experiments demonstrate that the proposed method can effectively enhance the performance of cross domain learning.