On Matching Interest Regions Using Local Descriptors - Can an Information Theoretic Approach Help?

This paper shows that the common task of interest region matching using local descriptors can be improved using a new similarity measure. The similarity measure is motivated by the information theoretic image alignment that maximize mutual information between images. A property of the mutual information metric is that it does not only depend on how similar the signals are but also how complex they are. We present how similar logic can be applied to the standard SIFT descriptor. The results show improvement at almost no additional computational costs.