A Comparative Study among Pattern Classifiers in Interactive Image Segmentation

Edition of natural images usually asks for considerable userinvolvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside the object. Enhancement increases the dissimilarities between object and background and Extraction separates them. Enhancement is done by a fuzzy pixel classifier and it has a great impact in the number of markers required for extraction. In view of minimizing user involvement, we focus this paper on a comparative study among popular classifiers for enhancement, conducting experiments with several natural images and seven users.

[1]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[2]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[3]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Alexandre X. Falcão,et al.  Interactive volume segmentation with differential image foresting transforms , 2004, IEEE Transactions on Medical Imaging.

[5]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[6]  Jayaram K. Udupa,et al.  An ultra-fast user-steered image segmentation paradigm: live wire on the fly , 2000, IEEE Transactions on Medical Imaging.

[7]  Anderson Rocha,et al.  Object Delineation by -Connected Components , 2008, EURASIP J. Adv. Signal Process..

[8]  Fernand Meyer,et al.  Levelings, Image Simplification Filters for Segmentation , 2004, Journal of Mathematical Imaging and Vision.

[9]  Javier A. Montoya-Zegarra,et al.  Fast interactive segmentation of natural images using the image foresting transform , 2009, 2009 16th International Conference on Digital Signal Processing.

[10]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[11]  Jayaram K. Udupa,et al.  Ultrafast user-steered image segmentation paradigm: live-wire-on-the-fly , 1999, Medical Imaging.

[12]  João Paulo Papa,et al.  A Discrete Approach for Supervised Pattern Recognition , 2008, IWCIA.

[13]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.