Entropy and color correlation for image indexing
Seyoon Jeong, Kyuheon Kim, Byung Tae Chun, Jae Yeon Lee, Yonglae Bae · 2003
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 region. 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 consisting 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 in reducing the total retrieval time and improving the retrieval accuracy.