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With the development of face recognition using sparse representation based classification (SRC), many relevant methods have been proposed and investigated. However, when the dictionary is large and the representation is sparse, only a small proportion of the elements contributes to the l 1 -minimization. Under this observation, several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper, we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned, a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.

Keywords: HSV color histogram, HOG feature, shot boundary detection. Abstract.Spatial information can not be contained in the statistical feature of hue-saturation-value(HSV) color histogram, moreover, which is not robust for the videos with the similarity of background information and affected by illumination. Therefore, a new video shot segment method based on HSV histogram and Histogram of Gradient (HOG) feature is proposed. Firstly,HSV color histogram is used to detect the difference between two adjacent frames which is then compared with the adaptive threshold. After the initial detection of the shot boundary is finished, HOG feature of the video is adopted to make further shot boundary detection，which eliminates the wrong shot boundaries and adds missed shot boundaries. Experimental results indicate that the proposed method achieves higher accuracy and recall.

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