Quantitative analysis for image segmentation by granular computing clustering from the view of set

As partition method of set, granular computing clustering is applied to image segmentation evaluated by global consistency error, variation of Information, and Rand index from the view of set. Firstly, quantitative assessment of clustering is evaluated from the view of set. Secondly, granular comput...

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Bibliographic Details
Published in:Journal of algorithms & computational technology 2019, Vol.13
Main Authors: Liu, Hongbing, Diao, Xiaoyu, Guo, Huaping
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Color imagery
Computation
Consistency
Evaluation
Granular materials
Image segmentation
Norms
Quantitative analysis
Shape recognition
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Summary:As partition method of set, granular computing clustering is applied to image segmentation evaluated by global consistency error, variation of Information, and Rand index from the view of set. Firstly, quantitative assessment of clustering is evaluated from the view of set. Secondly, granular computing clustering algorithms are induced by the distance formulas, the granules with different shapes are defined as the forms of vectors by different distance norms, especially, the atomic granule is induced by a point of space, the union operator realizes the transformation between two granule spaces and is used to form granular computing clustering algorithms. Thirdly, the image segmentations by granular computing clustering are evaluated from the view of set, such as global consistency error, variation of Information, and Rand index. Segmentations of the color images selected from BSD300 are used to show the superiority and feasibility for image segmentation by granular computing clustering compared with Kmeans and fuzzy c-means by experiments.
ISSN:1748-3018
1748-3026
DOI:10.1177/1748301819833050