Abstract
In this paper, we propose assigning PLS based descriptors by SVM to obtain the representations of human action videos. First, in addition to the spatially gradient orientation, we add spatio-temporal gradient statistic to generate the extended Histogram of Oriented Gradient (HOG). Second, different from requently-used cuboid descriptors in which Principal Component Analysis (PCA) is applied for dimension reduction, the proposed features utilize the Partial Least Squares (PLS) method for better performance. Then, we apply a multi-class SVM for assignment instead of assigning descriptors to the nearest (Euclidean distance) visual word in traditional Bag of Visual Words (BOVW) framework. Finally, the K-nearest neighbor algorithm is used to classify the histogram of visual words. The experimental results on the facial expression dataset and KTH human activity dataset validate the effectiveness of our proposed method.
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Acknowledgments
The work was supported in part by National Natural Science Foundation of China under Grant No. 61305058, No. 61375001, Natural Science Foundation of Jiangsu Province of China under Grant No. BK20130471 and No. BK20140638, China Postdoctoral Science Foundation under grant No.2013M540404, Jiangsu Planned Projects for Postdoctoral Research Funds under grant No.1401037B, open fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education under Grant No.MCCSE2013B01, the Open Project Program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No. CDLS-2014-04), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the Fundamental Research Funds for the Central Universities.
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Sheng, J., Sheng, B., Yang, W., Sun, C. (2015). Assigning PLS Based Descriptors by SVM in Action Recognition. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_16
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DOI: https://doi.org/10.1007/978-3-319-23989-7_16
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