Weighted entropy for segmentation evaluation

In many image, video and computer vision systems the image segmentation is an essential part. Significant research has been done in image segmentation and a number of quantitative evaluation methods have already been proposed in the literature. However, often the segmentation evaluation is subjective that means it has been done visually or qualitatively. A segmentation evaluation method based on entropy is proposed in this work which is objective and simple to implement. A weighted self and mutual entropy are proposed to measure the dissimilarity of the pixels among the segmented regions and the similarity within a region. This evaluation technique gives a score that can be used to compare different segmentation algorithms for the same image, or to compare the segmentation results of a given algorithm with different images, or to find the best suited values of the parameters of a segmentation algorithm for a given image. The simulation results show that the proposed method can identify over-segmentation, under-segmentation, and the good segmentation.