In this paper, we study an information theoretic approach to image similarity measurement for content-base image retrieval. In this novel scheme, similarities are measured by the amount of information the images contained about one another mutual information (MI). The given approach is based on the premise that two similar images should have high mutual information, or equivalently, the querying image should convey high information about those similar to it. The method first generates a set of statistically representative visual patterns and uses the distributions of these patterns as images content descriptors. To measure the similarity of two images, we develop a method to compute the mutual information between their content descriptors. Two images with larger descriptor mutual information are regarded as more similar. We present experimental results, which demonstrate that mutual information is a more effective image similarity measure than those have been used in the literature such as Kullback-Leibler divergence and L2 norms.
This paper proposed a novel solution to track human face obscured largely in an image on the basis of Mean Shift Tracing Algorithm (MSTA). The improved approach aims to update the target model in real-time during the whole tracking process to avoid target losing. Local Binary Pattern (LBP) theory is chosen to improve the original MSTA here. The experimental result shows that our new solution has a better performance in target tracking under situations like face rotation and occlusion as well as in fast acquisition when faces reappear on the screen.
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