Automatic identification of important persons from news image collections

In order to automatically identify the important persons in news images and solve the diversity problem of visual distribution due to different factors such as expression, illumination and pose, we propose the method of automatic person identification based on multi-modal information fusion. First, for each target name, we find its corresponding face image subset and establish the mapping between the name and the faces. Second, we calculate the similarity in the text space as well as in the visual space, and cluster each face image subset. Finally, we exploit the weighted Borda method to fuse the similarity order of text space and visual space. The experiments are performed on the data set including approximately half a million news images from Yahoo! news, and the results show that the proposed method achieves significant improvement over the clustering-only methods.