Image change detection from difference image through deterministic simulated annealing



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Cruz García, Jesús Manuel de la and Pajares Martinsanz, Gonzalo and Ruz Ortíz, José Jaime (2009) Image change detection from difference image through deterministic simulated annealing. Pattern Analysis and Applications, 12 (2). pp. 137-150. ISSN 1433-7541

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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.

Item Type:Article
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© Springer-Verlag London Limited 2008.
Part of the work has been performed under project no. 143/2004 Fundacion General UCM. The authors are also grateful to the referees for their constructive criticism and suggestions on the original version of this paper.

Uncontrolled Keywords:Unsupervised Change Detection, Automatic Change Detection, Markov Random-Fields, Surveillance Systems, Sequences, Optimization, Illumination, Algorithms, Tracking, Objects
Subjects:Sciences > Computer science
ID Code:22564
Deposited On:01 Aug 2013 12:07
Last Modified:31 Dec 2020 00:01

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