IMAGE RETRIEVAL BASED ON CLASSIFIED VECTOR QUANTIZATION USING COLOR LOCAL THRESHOLDING CLASSIFIER

A method of natural image classification by an effective color quadtree segmentation together with a more effective codebook with the color local thresholding classifier for content-based image retrieval (CBIR) is proposed. The vector quantization (VQ) based image retrieval schemes have good performance, but the importance of color edge intensive blocks is neglected. Our proposed method has two main improvements. First, quadtree segmentation based on both hue and gray-level information is applied to classify the blocks into the homogeneous and high-detail ones. Second, a color local thresholding classifier is proposed to further classify the high-detail blocks based on edge information. Simulation results show that our proposed scheme outperforms the existing methods, including the Quadtree CVQ-based scheme, the VQ-based scheme, and other methods.