Rough Entropy Clustering Algorithm in Image Segmentation

Rough Entropy Clustering Algorithm in Image Segmentation

Dariusz Malyszko, Jaroslaw Stepaniuk
ISBN13: 9781605663241|ISBN10: 1605663247|EISBN13: 9781605663258
DOI: 10.4018/978-1-60566-324-1.ch012
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MLA

Malyszko, Dariusz, and Jaroslaw Stepaniuk. "Rough Entropy Clustering Algorithm in Image Segmentation." Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation, edited by JingTao Yao, IGI Global, 2010, pp. 285-304. https://doi.org/10.4018/978-1-60566-324-1.ch012

APA

Malyszko, D. & Stepaniuk, J. (2010). Rough Entropy Clustering Algorithm in Image Segmentation. In J. Yao (Ed.), Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation (pp. 285-304). IGI Global. https://doi.org/10.4018/978-1-60566-324-1.ch012

Chicago

Malyszko, Dariusz, and Jaroslaw Stepaniuk. "Rough Entropy Clustering Algorithm in Image Segmentation." In Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation, edited by JingTao Yao, 285-304. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-324-1.ch012

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Abstract

Clustering understood as a data grouping technique represents fundamental procedures in image processing. The present chapter’s concerns are combining the concept of rough sets and entropy measures in the area of image segmentation. In this context, comprehensive investigations into rough set entropy based clustering image segmentation techniques have been performed. Segmentation presents low-level image transformation routines concerned with image partitioning into distinct disjoint and homogenous regions. In the area of segmentation routines, threshold based algorithms and clustering algorithms most often are applied in practical solutions when there is a pressing need for simplicity and robustness. Rough entropy threshold based segmentation algorithms simultaneously combine optimal threshold determination with rough region approximations and region entropy measures. In the present chapter, new algorithmic schemes RECA in the area of rough entropy based partitioning routines have been proposed. Rough entropy clustering incorporates the notion of rough entropy into clustering models, taking advantage of dealing with some degree of uncertainty in analyzed data. RECA algorithmic schemes performed usually equally robust compared to standard k-means algorithms. At the same time, in many runs they yielded slightly better performances making possible future implementation in clustering applications.

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