Efficient Image Retrieval Based on Quantized Histogram Texture Features in DCT Domain

Huge number of images is available on the internet. Efficient and effective retrieval system is needed to retrieve these images by the contents or features of the images like color, texture and shape. This system is called content based image retrieval (CBIR). Conventionally features are extracted from images in pixel domain. But at present almost all images are represented in compressed form using DCT (Discrete Cosine Transformation) blocks transformation. Some critical information is removed in compression and only perceptual information is left which has significant attraction for information retrieval in compressed domain. In this paper we study the problem that how to retrieve perceptual information in compressed domain JPEG such that to improve image retrieval. Our approach is based on quantized histogram statistical texture features in DCT blocks. We show that to get best image retrieval performance by extracting the statistical texture features of quantized histogram in DCT blocks using JPEG compressed format images. Experiments on the Corel animal database using the proposed approach, give results which show that the statistical texture features of histogram are robust in retrieval of images. This shows that texture features in local compression is a significant step for effective image retrieval.