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- W91931225 abstract "Abstract Considering the problems that initial clustering center selection is very sensitive to clustering results, it is easy to fall into local optimum and only globular clusters can be found for the k-means algorithm in human action recognition. This paper presents a method based on maximum entropy fuzzy clustering and a new interest points detecting method for human action recognition. Firstly, the interest points of the videos are detected by the new approach, and the 3D-SIFT features of the points are extracted, and then, the features of the training videos are clustered generating video words based on maximum entropy fuzzy clustering algorithm to construct the codebook. In the end, the histogram based on the codebook for every video is built, which is used to classify targets in the SVM multi-class classifier. Experimental results show that the proposed method can effectively improve the expression ability of the codebook and the efficiency of the recognition of human action.KeywordsAction recognitionDetection of interest pointsCodebookMaximum entropy fuzzy clustering" @default.
- W91931225 created "2016-06-24" @default.
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- W91931225 date "2014-01-01" @default.
- W91931225 modified "2023-10-16" @default.
- W91931225 title "Action Recognition Based on Maximum Entropy Fuzzy Clustering Algorithm" @default.
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- W91931225 doi "https://doi.org/10.1007/978-3-642-54924-3_15" @default.
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