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arXiv · 1603.04015

Learning zeroth class dictionary for human action recognition

Jia-xin Cai·Xin Tang·Lifang Zhang·Guocan Feng
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Abstract

In this paper, a discriminative two-phase dictionary learning framework is proposed for classifying human action by sparse shape representations, in which the first-phase dictionary is learned on the selected discriminative frames and the second-phase dictionary is built for recognition using reconstruction errors of the first-phase dictionary as input features. We propose a "zeroth class" trick for detecting undiscriminating frames of the test video and eliminating them before voting on the action categories. Experimental results on benchmarks demonstrate the effectiveness of our method.

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Jia-xin Cai, Xin Tang, Lifang Zhang, Guocan Feng. 2016-03-13. Learning zeroth class dictionary for human action recognition. https://arxiv.org/abs/1603.04015

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