Generic and Fully Automatic Content-Based Image Retrieval Architecture

Content-based retrieval requires the choice of distance functions for determining inter-image distances. Distance functions considered to be desirable for computing inter-image distances are often too expensive, computationally, to be used for on-line retrieval from large image databases. In this paper, we propose a generic and eecient content-based image retrieval architecture where the original images are mapped on to an abstract feature space such that the desired (or real) inter-image distances correspond to the distances between the vector representations of the images in the feature space. It is shown that it is more eecient to compute distances between these feature vectors and use them as estimates of the real distances. We have conducted experiments using color as the low-level feature. The results show a substantial reduction in the size of the feature space. The experimental results also indicate that high accuracy is achieved for the set of queries tested.