Advances in Information Retrieval

Volume 2997 of the series Lecture Notes in Computer Science pp 253-266

NN k Networks for Content-Based Image Retrieval

  • Daniel HeeschAffiliated withDepartment of Computing, Imperial College
  • , Stefan RügerAffiliated withDepartment of Computing, Imperial College

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This paper describes a novel interaction technique to support content-based image search in large image collections. The idea is to represent each image as a vertex in a directed graph. Given a set of image features, an arc is established between two images if there exists at least one combination of features for which one image is retrieved as the nearest neighbour of the other. Each arc is weighted by the proportion of feature combinations for which the nearest neighour relationship holds. By thus integrating the retrieval results over all possible feature combinations, the resulting network helps expose the semantic richness of images and thus provides an elegant solution to the problem of feature weighting in content-based image retrieval. We give details of the method used for network generation and describe the ways a user can interact with the structure. We also provide an analysis of the network’s topology and provide quantitative evidence for the usefulness of the technique.