Color indexing for efficient image retrieval

Abstract

Content based image retrieval is an active area of research. Many approaches have been proposed to retrieve images based on matching of some features derived from the image content. Color is an important feature of image content. The problem with many traditional matching-based retrieval methods is that the search time for retrieving similar images for a given query image increases linearly with the size of the image database. We present an efficient color indexing scheme for similarity-based retrieval which has a search time that increases logarithmically with the database size. In our approach, the color features are extracted automatically using a color clustering algorithm. Then the cluster centroids are used as representatives of the images in 3-dimensional color space and are indexed using a spatial indexing method that uses R-tree. The worst case search time complexity of this approach is O(nq log(N *navg)), where N is the number of images in the database, and nq and navg are the number of colors in the query image and the average number of colors per image in the database respectively. We present the experimental results for the proposed approach on two databases consisting of 337 Trademark images and 200 Flag images.