Content-based retrieval of segmented images

Most general content-based image retrieval techniques use colour and texture as main retrieval indices. A recent technique uses colour pairs to model distinct object boundaries for retrieval. These techniques have been applied to overall image contents without taking into account the characteristics of individual objects. While the techniques work well for the retrieval of images with similar overall contents (including backgrounds), their accuracies are limited because they are unable to take advantage of individual object's visual characteristics, and to perform object-level retrieval. This paper looks specifically at the use of colour-pair technique for fuzzy object-level image retrieval. Three extensions are applied to the basic colour-pair technique: (a) the development of a similarity-based ranking formula for colour-pairs matching; (b) the use of segmented objects for object-level retrieval; and (c) the inclusion of perceptually similar colours for fuzzy retrieval. A computer-aided segmentation technique is developed to segment the images' contents. Experimental results indicate that the extensions have led to substantial improvements in the retrieval performance. These extensions are sufficiently general and can be applied to other content-based image retrieval techniques.