Abstract
We present a novel genetic algorithm (GA) for video sequence segmentation. The novelty of the approach is that the mating rates such as crossover rate and mutation rate are not constant, but spatio-temporally varying. The variation of mating rates depends on the degree of activity of each chromosome in between the successive frames. The effectiveness of the proposed method will be extensively tested in the synthetic and natural video sequences and compared to several other GA-based segmentation method. The results show that the proposed approach is able to enhance the computational efficiency and the quality of the segmentation results than other methods.
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References
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)
Wu, G.K., Reed, T.R.: Image sequence processing using spatiotemporal segmentation. IEEE Trans. Circuits Syst. Video Technol. 9(5), 798–807 (1999)
Kim, E.Y., Hwang, S.W., Park, S.H., Kim, H.J.: Spatiotemporal Segmentation using Genetic Algorithms. Pattern Recognition 34(10), 2063–2066 (2001)
Bhandarkar, S.M., Zhang, H.: Image segmentation using evolutionary computation. IEEE Trans. Evolutionary Computation. 3(1), 1–21 (1999)
Andrey, P., Tarroux, P.: Unsupervised segmentation of Markov random field modeled textured images using selectionist relaxation. IEEE Trans. Pattern Anal. Machine Intell. 20(3), 659–673 (1998)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Liu, J., Yang, Y.H.: Multiresoultion color image segmentation. IEEE Trans. PAMI 16(7), 689–700 (1994)
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© 2003 Springer-Verlag Berlin Heidelberg
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Kim, E.Y., Park, S.H. (2003). A Genetic Algorithm with Automatic Parameter Adaptation for Video Segmentation. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_30
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DOI: https://doi.org/10.1007/978-3-540-45179-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40730-0
Online ISBN: 978-3-540-45179-2
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