In this paper, we study disjoint information as a metric for image comparison and its applications in image matching, alignment, and video tracking. Disjoint information is the joint entropy of random variables excluding the mutual information. This measure of statistical dependence and information redundancy satisfies more rigorous metric conditions than mutual information. For image comparison, compound disjoint information is derived from the marginal densities of the image distributions. By using marginal densities other than color histograms, it can overcome the difficulties (such as a lack of spatial information) inherent in histogram-based mutual information methods and enrich the vocabulary of image description. Disjoint information is not sensitive to illumination and appearance changes, and it is particularly suited for multimodal applications.