Weighted entropy for segmentation evaluation

• A novel objective image segmentation evaluation method based on weighted self and mutual entropy.
• Measure dissimilarity of the pixels among the segmented regions and similarity within a region.
• Gives a score to rank different algorithms or to optimally set the parameters of a given algorithm.
• The given score exhibits the degree of similarity and dissimilarity.

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.