Abstract
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.
Similar content being viewed by others
References
Aach T, Kaup A (1995) Bayesian algorithms for adaptive change detection in image sequences using Markov Random fields. Signal Process Image Commun 7:147–160
Bosc M, Heitz F, Armspach JP, Namer I, Gounot D, Rumbach L (2003) Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution. Neuroimage 20:643–656
Bruzzone L, Fernández-Prieto D (2000a) An adaptive parcel-based technique for unsupervised change detection. Int J Remote Sensing 21(4):817–822
Bruzzone L, Fernández-Prieto D (2000b) Automatic analysis of the difference Image for unsupervised change detection. IEEE Trans. Geosci Remote Sensing 38(3):1171–1182
Bruzzone L, Fernández-Prieto D (2002) An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Trans Image Process 11(4):452–466
Carlotto MJ (2005) A cluster-based approach for detecting man-made objects and changes in imagery. IEEE Trans Geosci Remote Sensing 43(2):374–387
Chang CC, Chia TL, Yang CK (2005) Modified temporal difference method for change detection. Opt Eng 44(2):1–10
Desurmont M, Bastide M, Chaudy C, Parisot D, Delaigle JF, Macq B (2005) Image analysis architectures and techniques for intelligent surveillance systems. IEE Proc Vis Image Signal Process 152(2):224–231
Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
Dunmur AP, Titterington DM (1998) Mean fields and two-dimensional Markov random fields in image analysis. Pattern Anal Appl 1(4):248–260
Durucam E, Ebrahimi T (2001) Change detection and background extraction by linear algebra. Proc IEEE 89(10):1368–1381
Fang CY, Cheng SW, Fuh CS (2003) Automatic change detection of driving environments in a vision-based driver assistance system. IEEE Trans Neural Netw 14(3):646–657
Geman S, Geman G (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741
Hajek B (1988) Cooling schedules for optimal annealing. Math Oper Res 13:311–329
Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Co, New York
Hsu YZ, Nagel HH, Reckers G (1984) New likelihood test methods for change detection in image sequences. Comput Vis Graph Image Process 26:73–106
Jain Z and Chau Y (1995) Optimum multisensor data fusion for image change detection. IEEE Trans Syst Man Cybern 25(9):1340–1347
Kapur J, Sahoo P, Wong A (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285
Kasetkasem T, Varshney PK (2002) An image change detection algorithm based on Markov random field models. IEEE Trans Geosci Remote Sensing 40(8):1815–1823
Kirkpatrick S (1984) Optimization by simulated annealing: quantitative studies. J Stat Phys 34:975–984
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Laarhoven van PMJ, Aarts EHL (1989) Simulated annealing: theory and applications. Kluwer, Holland
Liu Q. (2005) New change detection models for object based encoding of patient monitoring video. PhD Thesis, School of Engineering. University of Pitsburg
Liu SC, Fu CV, Chang S (1998) Statistical change detection with moments under time-varying illumination. IEEE Trans Image Process 7(9):1258–1268
Liu Q, Sclabassi RJ, Li CC, Sun M (2005) An application of MAP-MRF to change detection in image sequence based on mean field theory. EURASIP J Appl Signal Process 2005:1956–1968
Lu T, Suganthan PN (2004) An accumulation algorithm for video shot boundary detection. Multimedia Tools and Appl 22:89–106
Pajares G (2006) A Hopfield neural network for image change detection. IEEE Trans Neural Netw (in press)
Pajares G, Ruz JJ, Cruz JM (2005) Performance analysis of homomorphic systems for image change detection. In: Marques JS, Pérez de la Blanca N, Pina P (eds) Pattern recognition and image analysis. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, pp 563–570
Paragios N, Deriche R (2000) Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Anal Mach Intell 22(3):266–280
Radke RJ, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307
Rosin PL, Ioannidis E (2003) Evaluation of global image thresholding for change detection. Pattern Recogn Lett 24:2345–2356
Skifstad K, Jain R (1989) Illumination independent change detection from real world images sequences. Comput Vis Graph Image Process 46(9):387–399
Sneath P, Sokal R (1973) Numerical taxonomy: the principle and practice of numerical classification. W.H. Freeman, San Francisco
Starck JL, Murtagh F, Bijaoui A (2000) Image processing and data analysis: the multiscale approach. Cambridge University Press, Cambridge
Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757
Stringa E, Regazzoni CS (2000) Real-time video shot detection for scene surveillance applications. IEEE Trans Image Process 9:69–79
Valera M, Velastin SA (2005) Intelligent distributed surveillance systems: a review. IEE Proc Vis Image Signal Process 152(2):192–204
Wu QZ, Cheng HY, Jeng BS (2005) Motion detection via change-point detection for cumulative histograms of ratio images. Pattern Recogn Lett 26:555–563
Acknowledgments
Part of the work has been performed under project no. 143/2004 Fundación General UCM. The authors are also grateful to the referees for their constructive criticism and suggestions on the original version of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pajares, G., Ruz, J.J. & de la Cruz, J.M. Image change detection from difference image through deterministic simulated annealing. Pattern Anal Applic 12, 137–150 (2009). https://doi.org/10.1007/s10044-008-0110-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-008-0110-5