Coarse detection and fine color description for region-based image queries

In content-based image retrieval systems, region-based queries allow more precise search than global ones. The user can retrieve similar regions of interest regardless their background in images. The definition of regions in thousands of generic images is a difficult key point, since it should not need user interaction for each image, and nevertheless be as close as possible to regions of interest (to the user). In this paper 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, which has the advantage to automatically determine the optimal number of classes, is used. The second key point is the region description which must be finer for regions than for images. We present a region descriptor of fine color variability: the adaptive distribution of color shades. This color description is finer and more accurate than existing region color descriptors.