Abstract：Since term frequency is not considered by traditional information gain in Bag-of-Words (BoW) model, a new visual dictionary constructing method based on improved information gain was proposed to improve the human actions recognition accuracy. Firstly, spatio-temporal interest points of human action video were extracted by using 3D Harris, then clustered by K-means to construct initial visual dictionary. Secondly, concentration of term frequency within cluster and dispersion of term frequency between clusters were introduced to improve the information gain, which was used to compute the initial dictionary; then the visual words with larger information gain were selected to build a new visual dictionary. Finally, the human actions were recognized based on Support Vector Machine (SVM) using the improved information gain. The proposed method was verified by human actions recognition of KTH and Weizmann databases. Compared with the traditional information gain, the actions recognition accuracy was increased by 1.67% and 3.45% with the dictionary constructed by improved information gain. Experimental results show that the visual dictionary of human actions based on improved information gain increases the accuracy of human actions recognition by selecting more discriminate visual words.
吴峰, 王颖. 基于改进信息增益的人体动作识别视觉词典建立[J]. 计算机应用, 2017, 37(8): 2240-2243.
WU Feng, WANG Ying. Visual dictionary construction for human actions recognition based on improved information gain. Journal of Computer Applications, 2017, 37(8): 2240-2243.
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