Content based image retrieval (CBIR) is a method of retrieving images from large image resource, which has been found to be very effective. To represent images in terms of their features, CBIR involves the use of low-level image features like, colour, texture, shape, and spatial location, etc. To improve existing CBIR performance, it is very important to find effective and efficient feature extraction mechanisms. Texture effectively describes the distinguishing characteristics between images. It is one of the most important and prominent properties of an image. A variety of techniques have been developed for extracting texture features, broadly classified into the spatial and spectral methods. Though many works on texture classification and representation have already been done, it is still an open issue. Vector Quantization (VQ) is an efficient and simple approach for data compression. Therefore, the computational cost of CBIR system can be reduced by using vector quantization. In this paper we have provided the overview of different methods for textured based CBIR system and also discussed how its performance can be improved by vector quantization.

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Cbir Texture Special Spectral Vector Quantization