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Graph Theoretic Approaches for Image Analysis

Graph Theoretic Approaches for Image Analysis

Biplab Banerjee, Sudipan Saha, Krishna Mohan Buddhiraju
Copyright: © 2017 |Pages: 32
ISBN13: 9781522517764|ISBN10: 1522517766|EISBN13: 9781522517771
DOI: 10.4018/978-1-5225-1776-4.ch008
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MLA

Banerjee, Biplab, et al. "Graph Theoretic Approaches for Image Analysis." Intelligent Multidimensional Data Clustering and Analysis, edited by Siddhartha Bhattacharyya, et al., IGI Global, 2017, pp. 193-224. https://doi.org/10.4018/978-1-5225-1776-4.ch008

APA

Banerjee, B., Saha, S., & Mohan Buddhiraju, K. (2017). Graph Theoretic Approaches for Image Analysis. In S. Bhattacharyya, S. De, I. Pan, & P. Dutta (Eds.), Intelligent Multidimensional Data Clustering and Analysis (pp. 193-224). IGI Global. https://doi.org/10.4018/978-1-5225-1776-4.ch008

Chicago

Banerjee, Biplab, Sudipan Saha, and Krishna Mohan Buddhiraju. "Graph Theoretic Approaches for Image Analysis." In Intelligent Multidimensional Data Clustering and Analysis, edited by Siddhartha Bhattacharyya, et al., 193-224. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1776-4.ch008

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Abstract

Different graph theoretic approaches are prevalent in the field of image analysis. Graphs provide a natural representation of image pixels exploring their pairwise interactions among themselves. Graph theoretic approaches have been used for problem like image segmentation, object representation, matching for different kinds of data. In this chapter, we mainly aim at highlighting the applicability of graph clustering techniques for the purpose of image segmentation. We describe different spectral clustering techniques, minimum spanning tree based data clustering, Markov Random Field (MRF) model for image segmentation in this respect.

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