Image change detection from difference image through deterministic simulated annealing

This paper proposes an automatic method based on the deterministic simulated annealing (DSA) approach for solving the image change detection problem between two images where one of them is the reference image. Each pixel in the reference image is considered as a node with a state value in a network of nodes. This state determines the magnitude of the change. The DSA optimization approach tries to achieve the most network stable configuration based on the minimization of an energy function. The DSA scheme allows the mapping of interpixel contextual dependencies which has been used favorably in some existing image change detection strategies. The main contribution of the DSA is exactly its ability for avoiding local minima during the optimization process thanks to the annealing scheme. Local minima have been detected when using some optimization strategies, such as Hopfield neural networks, in images with large amount of changes, greater than the 20%. The DSA performs better than other optimization strategies for images with a large amount of changes and obtain similar results for images where the changes are small. Hence, the DSA approach appears to be a general method for image change detection independently of the amount of changes. Its performance is compared against some recent image change detection methods.