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Video Concept Detection Using Support Vector Machine with Augmented Features

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3 Author(s)
Xinxing Xu ; Sch. of Comput. Eng., Nanyang Technologicial Univ., Singapore, Singapore ; Dong Xu ; Tsang, I.W.

In this paper, we present a direct application of Support Vector Machine with Augmented Features (AFSVM) for video concept detection. For each visual concept, we learn an adapted classifier by leveraging the pre-learnt SVM classifiers of other concepts. The solution of AFSVM is to re-train the SVM classifier using augmented feature, which concatenates the original feature vector with the decision value vector obtained from the pre-learnt SVM classifiers in the Reproducing Kernel Hilbert Space (RKHS). The experiments on the challenging TRECVID 2005 dataset demonstrate the effectiveness of AFSVM for video concept detection.

Published in:

Image and Video Technology (PSIVT), 2010 Fourth Pacific-Rim Symposium on

Date of Conference:

14-17 Nov. 2010