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Augmenting bag-of-words: a robust contextual representation of spatiotemporal interest points for action recognition

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Abstract

Although traditional bag-of-words model, together with local spatiotemporal features, has shown promising results for human action recognition, it ignores all structural information of features, which carries important information of motion structures in videos. Recent methods usually characterize the relationship of quantized spatiotemporal features to overcome this drawback. However, the propagation of quantization error leads to an unreliable representation. To alleviate the propagation of quantization error, we present a coding method, which considers not only the spatial similarity but also the reconstruction ability of visual words after giving a probabilistic interpretation of coding coefficients. Based on our coding method, a new type of feature called cumulative probability histogram is proposed to robustly characterize contextual structural information around interest points, which are extracted from multi-layered contexts and assumed to be complementary to local spatiotemporal features. The proposed method is verified on four benchmark datasets. Experiment results show that our method can achieve better performance than previous methods in action recognition.

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Notes

  1. For the case of \(k=1\), CPH features based on our coding method is actually based on hard-assignment coding method and we use the result of hard-assignment coding method as the accuracy of \(k = 1\).

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Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities of China under Grant 106112013CDJZR120014 and Scientific and Technological Research Program of Chongqing Municipal Education Commission of China under Grant KJ1401207.

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Authors and Affiliations

  1. Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China

    Yang Li, Junyong Ye, Tongqing Wang & Shijian Huang

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  1. Yang Li
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  2. Junyong Ye
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  3. Tongqing Wang
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Correspondence to Junyong Ye.

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Li, Y., Ye, J., Wang, T. et al. Augmenting bag-of-words: a robust contextual representation of spatiotemporal interest points for action recognition. Vis Comput 31, 1383–1394 (2015). https://doi.org/10.1007/s00371-014-1020-8

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