Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1.49MB)
This is an Open Access publication published under CSC-OpenAccess Policy.

PUBLICATIONS BY COUNTRIES

Top researchers from over 74 countries worldwide have trusted us because of quality publications.

United States of America
United Kingdom
Canada
Australia
Malaysia
China
Japan
Saudi Arabia
Egypt
India
Graph Theory Based Approach For Image Segmentation Using Wavelet Transform
Vikramsingh R. Parihar, Nileshsingh V. Thakur
Pages - 255 - 277     |    Revised - 10-08-2014     |    Published - 15-09-2014
Volume - 8   Issue - 5    |    Publication Date - September / October 2014  Table of Contents
MORE INFORMATION
KEYWORDS
Segmentation, Graph Theory, Threshold, Wavelet Transform.
ABSTRACT
This paper presents the image segmentation approach based on graph theory and threshold. Amongst the various segmentation approaches, the graph theoretic approaches in image segmentation make the formulation of the problem more flexible and the computation more resourceful. The problem is modeled in terms of partitioning a graph into several sub-graphs; such that each of them represents a meaningful region in the image. The segmentation problem is then solved in a spatially discrete space by the well-organized tools from graph theory. After the literature review, the problem is formulated regarding graph representation of image and threshold function. The boundaries between the regions are determined as per the segmentation criteria and the segmented regions are labeled with random colors. In presented approach, the image is preprocessed by discrete wavelet transform and coherence filter before graph segmentation. The experiments are carried out on a number of natural images taken from Berkeley Image Database as well as synthetic images from online resources. The experiments are performed by using the wavelets of Haar, DB2, DB4, DB6 and DB8. The results are evaluated and compared by using the performance evaluation parameters like execution time, Performance Ratio, Peak Signal to Noise Ratio, Precision and Recall and obtained results are encouraging.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 B. Peng, L. Zhang and D. Zhang, “A survey of graph theoretical approaches to image segmentation”, Pattern Recognition, Vol. 46, pp.1020-1038, (2013).
2 B. Peng, L. Zhang, D. Zhang and J. Yang, “Image segmentation by iterated region merging with localized graph cuts”, Pattern Recognition Vol. 44, pp. 2527-2538, (2011).
3 W. Tao, F. Chang, L. Liu, H. Jin and T. Wang, “ Interactively multiphase image segmentation based on variation formulation and graph cuts”, Pattern Recognition Vol. 43,pp. 3208-3218, (2010).
4 J. Kim and K.Sang, “Color–texture segmentation using unsupervised graph cuts”, Pattern Recognition Vol. 42, pp. 735-750, (2009).
5 M. Bleyer and M. Gelautz, “Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions”, Signal Processing: Image Communication,Vol.22, pp.127-143, (2007).
6 J. Shi and J. Malik, “Normalized Cuts and Image Segmentation”, IEEE Transactions on pattern analysis and machine intelligence, Vol. 22, Issue No. 8, (2000).
7 S. Wang and J. M. Siskind, “Image Segmentation with Ratio Cut”, IEEE Transactions on pattern and machine intelligence, Vol. 25, Issue No. 6, (2003).
8 W. Tao, H. Jin, and Y. Zhang, “Color Image Segmentation Based on Mean Shift and Normalized Cuts”, IEEE Transactions on systems, man and cybernetics, Vol. 37, Issue No.5, (2007).
9 R. C. Wilson, E. R. Hancock, and B. Luo, “ Pattern Vectors from Algebraic Graph Theory”,IEEE Transactions on pattern analysis and machine intelligence, Vol. 27, Issue No 7,(2005).
10 Y. Yang, S. Han, T. Wang, W. Tao and X. Tai, “Multilayer graphcuts based unsupervised color–texture image segmentation using multivariate mixed student’s t-distribution and regional credibility merging”, Pattern Recognition, Vol. 46, pp. 1101-1124, (2013).
11 P.F.Felzenszwalb and D.P.Huttenlocher, “Efficient graph based image segmentation”,International Journal of Computer vision, Vol.59, Issue No.2, (2004).
12 M. Zhang and R. Alhajj, “Improving the Graph-Based Image Segmentation Method”, Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'O6), 0-7695-2728-0106, (2006).
13 J. Weickert, “Coherence-Enhancing Diffusion Filtering”, International Journal of Computer Vision, Vol. 31, pp. 111-127, (1999).
14 P. A. Khaire and N. V. Thakur, “ A Fuzzy Set Approach for Edge Detection”, International Journal of Image Processing (IJIP), Vol. 6, Issue No. 6, (2012).
15 http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/ (accessed on 24/04/2014)
16 http://fin6.com/2013/12/color/ (accessed on 24/04/2014)
17 http://clancyartpages.files.wordpress.com/2011/09/color_grid_dl.jpg (accessed on 24/04/2014)
18 http://www.clker.com/clipart-grayscale-flower-decoration.html (accessed on 24/04/2014)
19 http://furbeeconsulting.com/blog/ (accessed on 24/04/2014).
Mr. Vikramsingh R. Parihar
Department of PG Studies (Electrical and Electronics Engg.) Prof Ram Meghe Collge of Engineering and Management Badnera-Amravati. 444701, INDIA - India
vikramparihar05@gmail.com
Mr. Nileshsingh V. Thakur
Department of PG Studies (Electrical and Electronics Engg.) Prof Ram Meghe Collge of Engineering and Management Badnera-Amravati. 444701, INDIA - India