关键词: 时序上下文特征, 尺度不变特征变换（SIFT）特征, HSV颜色特征, 多核学习

Abstract: On the basis of traditional bag of word model, according to the spatial and semantic similarity between the key frames of adjacent lens, this paper brings a new video scene classification model. It divides video clips into many shots and extracts their key frames and makes the key frames a gauge. The next thing is that the key frames as an image block produces an image on time sequence. SIFT features and HSV feature are extracted. This paper embeds the SIFT features and HSV feature data into Hilbert space. Through multi kernel learning, the algorithm selects the appropriate kernel functions to train each image, and gets the classification model. Experiments show that the proposed algorithm for video classification can achieve better performance.

Key words: time series contextual character, Scale-Invariant Feature Transform（SIFT） character, HSV character, multi kernel learning