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Abstract
The bag-of-words(BoW) model usually causes large errors and weak discrimination in video representation in video action recognition,and affects the human action recognition accuracy.To solve this problem,a discriminative sparse coding(DSC) video representation algorithm is proposed.It’s a sparse coding framework involving a Fisher discriminative analysis to encode video local spatial-temporal features and increase the video sparse representation discrimination.And an online discriminative dictionary learning algorithm is also proposed to train a dictionary from massive video data.The experiments show that,comparing with the existing algorithms,the proposed algorithm effectively improves human action recognition accuracy.
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