A relative entropy-based approach to image thresholding

In this paper, we present a new image thresholding technique which uses the relative entropy (also known as the Kullback-Leiber discrimination distance function) as a criterion of thresholding an image. As a result, a gray level minimizing the relative entropy will be the desired threshold. The proposed relative entropy approach is different from two known entropy-based thresholding techniques, the local entropy and joint entropy methods developed by N. R. Pal and S. K. Pal in the sense that the former is focused on the matching between two images while the latter only emphasized the entropy of the co-occurrence matrix of one image. The experimental results show that these three techniques are image dependent and the local entropy and relative entropy seem to perform better than does the joint entropy. In addition, the relative entropy can complement the local entropy and joint entropy in terms of providing different details which the others cannot. As far as computing saving is concerned, the relative entropy approach also provides the least computational complexity.