NN^k networks for Content-Based Image Retrieval

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

Graph theoretic foundations of pathfinder networks

AbstractThis paper is primarily expository, relating elements of graph theory to a computational theory of psychological similarity (or dissimilarity). A class of networks called Pathfinder networks (PFNETs) is defined. PFNETs are derived from estimates of dissimilarity for pairs of entities. Thus, PFNETs can be used to reveal aspects of the structure inherent in a set of pairwise estimates of dissimilarity. In order to accommodate different assumptions about the nature of the measurement scale (i.e. ordinal, interval, ratio) underlying the data, the Minkowski r-metric (also known as the L norm) is adapted to computing distances in networks. PFNETs are derived from data by: (1) regarding the matrix of dissimilarities as a network adjacency matrix (the DATANET); (2) computing the distance matrix (or r-distance matrix using the Minkowski r-metric) of the DATANET and (3) reducing the DATANET by eliminating each arc that has weight greater than the r-distance between the nodes connected by the arc. PFNET properties of inclusion, relation to minimal spanning trees, and invariance under transformations of data are discussed