<title>Indexing and retrieval of color images using vector quantization</title>

Abstract

Image and Video indexing is becoming popular with the increasing volumes of visual information that is being stored and transmitted in various multimedia applications. An important focus of the upcoming MPEG 7 standard is on indexing and retrieval of multimedia data. The visual information can be indexed using the spatial (color, texture, shape, sketch, etc.) and temporal (motion, camera operations, etc.) features. Since multimedia data is likely to be stored in compressed form, indexing the information in compressed domain entails savings in compute time and storage space. In this paper, we present a novel indexing and retrieval technique using vector quantization of color images. Color is an important feature for indexing the visual information. Several color based indexing schemes have been reported in the recent literature. Vector Quantization (VQ) is a popular compression technique for low-power applications. Indexing the visual information based on VQ features such as luminance codebook and labels have also been recently presented in the literature. Previous VQ-based indexing techniques describes the entire image content by modeling the histogram of the image without taking into account the location of colors, which may result in unsatisfactory retrieval. We propose to incorporate spatial information in the content representation in VQ-compressed domain. We employ the luminance and chrominance codebooks trained and generated from wavelet-vector-quantized (WVQ) images, in which the images are first decomposed using wavelet transform followed by vector quantization of the transform coefficients. The labels, and the usage maps corresponding to the utilization pattern of codebooks for the individual images serve as indices to the associated color information contained in the images. Hence, the VQ compression parameters serve the purpose of indexing resulting in joint compression and indexing of the color information. Our simulations indicate superior indexing and retrieval performance at a reduced computational complexity. Performance analysis over a variety of color images using the proposed technique and other competing techniques in the literature is presented in the paper.