Image Retrieval Using Mutual Information

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1537-1543
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1537
• Online since: September 2013
• Authors: Ya Fen Wang*, Feng Zhen Zhang, Shan Jian Liu, Meng Huang
• Keywords: Content-Based Image Indexing and Retrieval (CBIR), Entropy, Image Retrieval, Kullback-Leibler Divergence (KLD), Mutual Information (MI)

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