Video shot classification based on sparse representation of dictionary optimized
基于字典优化的稀疏表示的视频镜头分类

In order to overcome the ineffective classification results of the redundant dictionary in the sparse representation-based classifier, this paper presented a sparse representation algorithm based on dictionary optimization. The algorithm developed a new classification discriminate rules based on sparse representation. It optimized the dictionary by the method of minimizing the average of the in-class Euclidean distance and maximized the average of the between-class Euclidean distance, formed the optimized dictionary and presented the features based on sparse representation. And the algorithm was applied on video shot to extract feature and classify based on sparse representation. The experimental results show that the recognition rate of feature extraction and classification on video shot based on the dictionary optimized by this method has been significantly improved.