A Hierarchical Multilevel Image Thresholding Method Based on the Maximum Fuzzy Entropy Principle

A Hierarchical Multilevel Image Thresholding Method Based on the Maximum Fuzzy Entropy Principle

Pearl P. Guan (City University of Hong Kong, Hong Kong) and Hong Yan (City University of Hong Kong, Hong Kong & University of Sydney, Australia)
Copyright: © 2013 |Pages: 29
DOI: 10.4018/978-1-4666-3994-2.ch016
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Abstract

Image thresholding and edge detection are crucial in image processing and understanding. In this chapter, the authors propose a hierarchical multilevel image thresholding method for edge information extraction based on the maximum fuzzy entropy principle. In order to realize multilevel thresholding, a tree structure is used to express the histogram of an image. In each level of the tree structure, the image is segmented by three-level thresholding based on the maximum fuzzy entropy principle. In theory, the histogram hierarchy can be combined arbitrarily with multilevel thresholding. The proposed method is proven by experimentation to retain more edge information than existing methods employing several grayscale images. Furthermore, the authors extend the multilevel thresholding algorithm for color images in the application of content-based image retrieval, combining with edge direction histograms. Compared to using the original images, experimental results show that the thresholding images outperform in achieving higher average precision and recall.
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Background

There is extensive literature on image thresholding and content-based image retrieval. Considering the purpose of this chapter, we introduce the image thresholding techniques according to different classifications. The literature review mainly concentrates on the last category although some related papers are given to illustrate the classification. As for content-based image retrieval, the literature is described according to various kinds of feature representations, such as color, shape, etc.

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