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.

Integration of optimized local directional weber pattern with faster region convolutional neural network for enhanced medical image retrieval and classification

“…The mutual information 33 is computed for recognizing the identical information contents among any two images. Here, the mutual information among the query and the other images present in the database are estimated for recognizing the MIC value.…”

“…The computer-oriented medical image analysis approach supports the technicians and the medical experts for enhancing their disease diagnosis on the basis of the crucial inputs provided by the computer system. The medical image retrieval 32,33 is considered as the challenging and primary problem for the health as well as the medical informatics. In the case of image retrieval, the optimal image matching together with the descriptors is considered from a database over a query image on the basis of the content similarity among the database and the query images.…”