A New Human Action Recognition Method Based On Discriminant Clustering

The research, in the field of video classification and human action recognition, is deepen year by year. Related industries, such as video surveillance, intelligent security and human-computer interaction, developed rapidly in the past years. Human action recognition and video classification technology in the near future will have a broader market space. Action recognition algorithm is related to the video data preprocessing,feature extraction, feature coding, data dimensionality reduction, clustering analysis, model learning and many other areas. And today, behavior recognition is focusing on constructing classification model and middle semantic information extraction instead of designing or building the low-level features. In the perspective of middle-level semantic features, this thesis will summarize and analyze the existing research results and existing problems that related, and complete the following work.Firstly, the thesis summarized and analyzed the common clustering analysis algorithm in behavior recognition. In view of some of traditional algorithms have the following problems, such as sensitive to initial setting, the number of cluster centers need artificial setting, the algorithm is easy to fall into local minimum, Euclidean distance metric for some clustering feature similarity is not an accurate measure, this thesis gives a hierarchical clustering analysis algorithm based on the category. The advantages of this clustering algorithm is that, this method allows the feature vectors belonging to the same cluster have a high degree of polymerization in the feature space, but have a high degree differences when feature vectors belonging to different clusters. For that, the algorithm calculates the measurement, which consisting of the polymerization metrics within the class and the dispersion metrics at inter-class, under different cluster number. By analyzing the changing trends of the metrics, consisted of the degree of polymerization within category and the degree of differences among the categories, under different cluster number, we can obtain the balance between the degree of polymerization of features within category and the differences that among the categories. In this way, we can balance the degree of polymerization within category and the degree of differences among the categories and get the number of clustering center adaptive.Secondly, this thesis also built a new framework of discriminative clustering analysisalgorithm. Because 1) the limitation of describing ability of low-level features, local features of the video, 2) the valid information is submerged in a flood of redundant data,hence the summary of the low level information refining is urgent and important. To obtain more effective middle semantic features, we propose a new framework of discriminative clustering analysis algorithm. The proposed clustering framework based on discriminative hierarchical clustering, which will removing the existing cluster clusters singular point, so that it can guarantee the purity of the clustering center anchor. At the same time, the request for the number of features belonging to the clusters anchor is proposed, the fewer the number of support points belonging to a cluster center, the weaker the cluster center corresponding to the behavior, so that the framework will eliminate the poly class center.Thus, the constraints of the number that belonging to the cluster center anchor can strengthen representative of a cluster center. In addition, in an iterative process, this algorithm will continue slashing cluster center anchor which is lack of discrimination.Therefore, discriminative clustering analysis algorithm framework to strike a cluster center not only has richer abundant semantic information, it will also have more excellent discrimination, representative and behavior category purity.Thirdly, we constructed a classification model that can train three-level semantic features at the same time, which add a category constraint CCLSVM(Category Constraint Latent Value Support Vector Machine). To optimize identification results of the low-level features,this thesis proposes high-level semantic features. Because the low-level features to identify and classify the video will occur the semantic information crossing, so it is easy to produce across the "semantic gap". For the establishment of semantic association, connecting the "semantic gap", this thesis presents discriminative model CC-LSVM in this way that three semantic information can be integrated used, multi-level semantic behavior classification model can be realized, and further optimize the recognition results.At the end of the thesis, it summarizes the important research contents of this thesis, and the future direction of exploration in action recognition with mid-level feature.