Color object indexing and retrieval in digital libraries
In our previous work, illumination invariant object recognition was achieved by normalizing the three color bands. We further employed the compressed histogram of the chromaticity to arrive at a valuable representation of an object which can facilitate high retrieval accuracy. The first shortcoming of this method lies in the usage of a uniform quantization scheme in obtaining the chromaticity, which is not in agreement with the perception of the human vision system. In this paper, we develop an approach using the CIE UCS transform to circumvent this problem. Second, instead of using uncompressed images to achieve the illumination in-variant indexing and retrieval, we carry out our indexing process directly in the DCT domain by using several coefficients from each macro-block. Third, in light of the special properties of the normalized chromaticity histogram frames, the foundation of the ensuing low-pass filtering, an additional step is inserted to render this frame smoother thus resulting in a better data reduction. Fourth, in order to facilitate efficient retrieval during data query phase, which is of utmost importance in digital libraries, the 36-dimensional model vectors as the indices of model images in digital libraries are clustered by use of vector quantization techniques. This clustering strategy reduces the searching space by order of magnitude. Desirable results have been observed from our experiments using the proposed color-object-indexing/retrieval algorithm.
digital libraries illumination invariance vector quantization content-based object indexing statistics of natural images