Region-based retrieval: coarse segmentation with fine color signature

The two major problems raised by a region-based image retrieval system are the automatic definition and description of regions. We first present a technique of unsupervised coarse detection of regions which improves their visual specificity. The segmentation scheme is based on the classification of local distributions of quantized colors (LDQC). The competitive agglomeration (CA) classification algorithm is used which has the advantage to automatically determine the optimal number of classes. Then, considering that region description which must be finer for regions than for images, we propose a region descriptor of fine color variability: the adaptive distribution of color shades (ADCS). Compared to existing color descriptors, the high color resolution of ADCS improves the perceptual similarity of retrieved regions.