Entropy-Based 2D Image Dissimilarity Measure

Traditional histogram or statistics based 2D image similarity/dissimilarity metrics fail to handle conjugate pair of black and white images, due to the lack of spatial information in the measurement. Recently proposed compression-based dissimilarity measure (CDM) based on the concept of Kolmogorov complexity has provided a different paradise for similarity measurement. However, without a clear definition on how to "concatenate" two 2D images, CDM has difficulties applying with 2D images directly. In this paper, we propose an entropy-based 2D image dissimilarity measure within the same Kolmogorov complexity paradise. The spatial relationship between images is embedded in our metric, and the actual compression of images is not needed once the entropy values are obtained. The proposed metric has been tested for scene change detection application, and encouraging results are presented here