Abstract
The goal of image segmentation in imaging science is to solve the problem of partitioning an image into smaller disjoint homogeneous regions that share similar attributes. The novel technique of the multiphase level set based on principal component analysis (PCA) with adaptively selecting dominant factors for color image segmentation in color spaces is studied here. And simultaneously, the final segmentation is completed by a simple labeling scheme. Then the comparative study of the refined Chan-Vese method is done in multiple color spaces. The experimental results illustrate that the multiphase Chan-Vese algorithm with or without PCA has good segmentation results with fine adaptability in RGB, CIE XYZ, NTSC and YCbCr color spaces where the results of test image changes little. Nevertheless, the h1h2h3 color space, produce poor segmentation on the reliability and accuracy of a set of test images by performance analysis with evaluation indicators.
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References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1987)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Osher, S., Sethian, J.A.: Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. Journal of Computational Physics 79, 12–49 (1988)
Caselles, V., Catté, F., Coll, T., Dibos, F.: A Geometric Model for Active Contours. Numerische Mathematik 66, 1–31 (1993)
Paragios, N., Deriche, R.: Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation. International Journal of Computer Vision, 223–247 (2002)
Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. of Computer Vision 69(3), 335–351 (2006)
Hajihashemi, M.R., El-Shenawee, M.: Shape Reconstruction Using the Level Set Method for Microwave Applications. IEEE Antennas and Wireless Propagation Letters 7, 92–96 (2008)
Vese, L.A., Chan, T.F.: Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. International Journal of Computer Vision 50(3), 271–293 (2002)
Liu, K., Du, Q., Yang, H., et al.: Optical Flow and Principal Component Analysis-Based Motion Detection in Outdoor Videos. Eurasip Journal on Advances in Signal Processing, no 680623 (2010)
Celik, T.: Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering. IEEE Geosciencs and Remote Sensing Letters 6(4), 772–776 (2009)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: The Berkeley Segmentation Dataset and Benchmark (January 6, 2011), http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/
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© 2011 Springer-Verlag Berlin Heidelberg
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Zhang, Y., Zhang, Y. (2011). Image Segmentation Using the Multiphase Level Set in Multiple Color Spaces. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_40
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DOI: https://doi.org/10.1007/978-3-642-23887-1_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23886-4
Online ISBN: 978-3-642-23887-1
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