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.%针对词袋(BoW)模型方法基于信息增益的视觉词典建立方法未考虑词频对动作识别的影响,为提高动作识别准确率,提出了基于改进信息增益建立视觉词典的方法.首先,基于3D Harris提取人体动作视频时空兴趣点并利用K均值聚类建立初始视觉词典;然后引入类内词频集中度和类间词频分散度改进信息增益,计算初始词典中词汇的改进信息增益,选择改进信息增益大的视觉词汇建立新的视觉词典;最后基于支持向量机(SVM)采用改进信息增益建立的视觉词典进行人体动作识别.采用KTH和Weizmann人体动作数据库进行实验验证.相比传统信息增益,两个数据库利用改进信息增益建立的视觉词典动作识别准确率分别提高了1.67％和3.45％.实验结果表明,提出的基于改进信息增益的视觉词典建立方法能够选择动作识别能力强的视觉词汇,提高动作识别准确率.
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