Zhong-Wei Li; Ming-Jiu Ni; Zhen-Kuan Pan

Variation Level Set Method for Multiphase Image Classification

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Author(s)

Zhong-Wei Li ^1,* Ming-Jiu Ni ¹ Zhen-Kuan Pan ²

1. College of Physics Science Graduate University of Chinese Academy of Science Beijing, China

2. College of Information Engineering, Qingdao University Qingdao, Shandong, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2011.05.08

Received: 14 Apr. 2011 / Revised: 20 May 2011 / Accepted: 22 Jun. 2011 / Published: 8 Aug. 2011

Index Terms

Partial differential equations, level set, image classification, boundary alignment, re-initialization

Abstract

In this paper a multiphase image classification model based on variation level set method is presented. In recent years many classification algorithms based on level set method have been proposed for image classification. However, all of them have defects to some degree, such as parameters estimation and re-initialization of level set functions. To solve this problem, a new model including parameters estimation capability is proposed. Even for noise images the parameters needn’t to be predefined. This model also includes a new term that forces the level set function to be close to a signed distance function. In addition, a boundary alignment term is also included in this model that is used for segmentation of thin structures. Finally the proposed model has been applied to both synthetic and real images with promising results.

Cite This Paper

Zhong-Wei Li,Ming-Jiu Ni,Zhen-Kuan Pan,"Variation Level Set Method for Multiphase Image Classification", IJIGSP, vol.3, no.5, pp.51-57, 2011. DOI: 10.5815/ijigsp.2011.05.08

Reference

[1]M. Berthod, Z. Kato, Y. Shan, and J. Zerubia, “Bayesian image classification using Markov random fields,” Image. Vis. Comput., vol. 14, pp. 285–295, May. 1996.

[2]C. Samson, L. Blanc-Féraud, G.Aubert, and J.Zerubia, “Multiphase evolution and variational image classification,” Tech. Rep. 3662, INRIA, Sophia Antipolis Cedex, France, Apr.1999.

[3]A. Hegerath, T. Deselaers,and H. Ney, “Patch-based object recognition using discriminatively trained Gaussian mixtures,” Br. Mach Vis Conf, Edinburgh, UK, pp. 519–528, 2006.

[4]I. Laptev, “Improvements of object detection using boosted histograms,” Br Mach Vis Conf, Edinburgh, UK, pp. 949-958, 2006.

[5]S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” Comput Soc Conf Comput.Vis. Pattern Recognit, Washington, USA, pp.2169-2178, 2006.

[6]J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, “Local features and kernels for classiﬁcation of texture and object categories: a comprehensive study,” Int. J. Comput. Vis., vol. 73, pp.213–218, 2007.

[7]S. Osher, J. A. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations”, J. Comput. Phys., vol. 79, pp.12-49, 1988.

[8]C. Samson, L.Blanc-Féraud, G.Aubert, and J.Zerubia, “A Level Set Model for Image Classification,” Int. J. Comput. Vis, vol. 40, pp. 187–197, 2000.

[9]C. Li, C. Xu, C. Gui and D.Martin, “Level set Evolution Without Reinitialization: A New Variontional Fomulation,” IEEE Int Conf. Comput. Vis. Pattern Recognit., San Diego, vol.1, pp.430-436, 2005.

[10]A. Vasilevskiy and K. Siddiqi, “Flux maximizing geometric flows,” IEEE Trans. Pattern Anal. Machine Intell., vol. 24, pp. 1565– 1578, 2002.

International Journal of Image, Graphics and Signal Processing (IJIGSP)