An internal self-similarities matching algorithm for IR/visual images based on shape

We present an efficient approach for measuring similarity between visual and IR images based on matching internal self-similarities. What is correlated across images is the internal layout of local self-similarities, even though geometric distortions and at multiple scales. These internal self-similarities are efficiently captured by a compact local "self-similarity descriptor". We compare our measure to commonly used SURF. Experimental results show that the proposed algorithm can realize the rotation invariance, scale invariance and robustness for occlusion. The proposed algorithm can match the shape in the IR and visible images efficiently and correctly.