NN^k networks for Content-Based Image Retrieval

Heesch, Daniel and Rüger, Stefan (2004). NN^k networks for Content-Based Image Retrieval. In: Advances in Information Retrieval, pp. 253–266.

DOI: https://doi.org/10.1007/978-3-540-24752-4_19

URL: http://dx.doi.org/10.1007/978-3-540-24752-4_19

Abstract

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 neighbour 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.

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About

• Item ORO ID
• 29871
• Item Type
• Conference or Workshop Item
• ISSN
• 0302-9743
• Extra Information
• Published in: S. McDonald and J. Tait (Eds.): ECIR 2004, LNCS 2997, pp. 253-266, 2004
• Keywords
• NN^k networks; image browsing; lateral browsing; image retrieval
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2004 Springer-Verlag
• Depositing User
• Stefan Rüger