Image indexing technique using entropy measures with a multilevel multiresolution approach

In this paper, we propose a new content-based indexing algorithm that utilizes pixel-wise entropy and extracts features such as color and entropy from an image as indices. We propose a technique that fulfills both global and regional searching. Global searching scheme utilizes entropy features with multilevel-multiresolution. As resolution of the image is reduced, another information of the image is revealed. As gray-level of the image is reduced, we see how large the gray- level differences are between neighboring pixels. Regional searching utilizes color features that are extracted from regions separated by entropy measures. Our algorithm provides not only the automated extraction of entropy-based regions but also the representation of their color contents. Thus, we can classify images using entropy and multiresolution multi-level based features. Various experiments show the promising future of the proposed algorithm.