Image indexing and retrieval in compressed domain using color clusters

A compressed domain color-based image indexing method that avoids overheads associated with full decompression and color space transformation by operating in the YCbCr space has been presented. The proposed method performs a hard segmentation of the color space into predefined clusters based on perceptual similarity. A three dimensional color descriptor is generated by extracting the average intensity value of the Y, Cb and Cr components for each 8X8 DCT block. Each blocks descriptor is subsequently allocated to its corresponding cluster, whose centroid is updated using a weighted mean. The similarity measure is evaluated using a compact four dimensional feature vector from each cluster, which consists of the quantized centroid co-ordinates and the percentage contribution to the image composition by the cluster. Retrieval is performed by comparing the feature attributes of the clusters having significant membership in the query image, to those with the database images; the obtained matches are then integrated to evaluate the final ranking. The experimental results of the proposed model on a database of 7380 images are reported.