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Linearized kernel dictionary learning with group sparse priors for action recognition
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DOI：10.1007/s00371-018-1603-x.png)
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Classification-driven dictionary has been successfully used in pattern recognition and computer vision in recent years. In this paper, a discriminative dictionary is constructed by concatenating all class specific sub-dictionaries and one sub-dictionary containing the common patterns. To further enhance the discriminative power, we also propose to use group sparse priors in the coding stage of the dictionary learning process. A kernel dictionary is learned to solve the same direction distribution problem existing in the traditional dictionary learning framework. Actually, the kernel dictionary is learned in a linearized manner by using virtual features. We evaluate our method on three public action datasets including facial expression, Hand Gesture and UCF Sports. Experimental results demonstrate that our method can achieve the better or at least competitive performance when compared with other action recognition methods.
Keyword:
Dictionary learning
Action recognition
Linearized kernel method
Group sparse
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