Shashank Srivastava and Sahil Suneja
Advisor: Dr. Arnab Bhattacharya, IIT Kanpur
Color is an important attribute of visual information, and hence can be a very useful attribute for image matching and retrieval, instead of dominantly shape matching approaches. Color is largely independent of view and resolution and also serves as a local identifying feature. Color based indexed searches have previously relied primarily on histogram-based approaches or color clustering for similarity searches. We propose a new indexing approach based on a large hierarchical colormap, where similarity can be given simply as an inner product. A feature vector is used to represent the color content of an image, and is used as a preliminary index to prune most image searches. In the second step, the pruned images (with a greater than threshold color-match) are matched with the input object for individual features by L1 norm. The two-step approach is seen to improve search time with a high selectivity in the ﬁrst step, and eﬃcient results on an image-dataset compiled from the Alamy database.