Entropy and color correlation features for image indexing

This paper presents a new indexing system, which divides an image into higher and lower entropy regions, and calculates the color correlation features between each type of regions. Thus, we can improve the discrimination power of color indexing techniques. The indexing system proposed in this paper has three important properties. The first is an entropy feature. An image is divided by an entropy feature into two regions, lower and higher entropy regions in order to avoid the problems caused by general global features. The second is a color correlation feature. This paper uses 2-dimensional probability distribution functions of an image to obtain color moment features, and thus, obtain more information than using 1-dimensional probability distribution functions (i.e. histogram). The last is the retrieval procedure consisted of two steps: firstly, a simple retrieval algorithm is applied to all the images in a database, and secondly, only the results of the previous retrieval are searched. Thus, it can help reducing the total retrieval time and improving the retrieval accuracy.