Fast computation of similarity based on jaccard coefficient for composition-based image retrieval

Abstract

This paper proposes an algorithm and data structure for fast computation of similarity based on Jaccard coefficient to retrieve images with regions similar to those of a query image. The similarity measures the degree of overlap between the regions of an image and those of another image. The key idea for fast computation of the similarity is to use the runlength description of an image for computing the number of overlapped pixels between the regions. We present an algorithm and data structure, and do experiments on 30,000 images to evaluate the performance of our algorithm. Experiments showed that the proposed algorithm is 5.49 (2.36) times faster than a naive algorithm on the average (the worst). And we theoretically gave fairly good estimates of the computation time. © 2009 Springer-Verlag Berlin Heidelberg.