Image segmentation based on histogram analysis utilizing the cloud model

Authors:
Kun Qin;Kai Xu;Feilong Liu;Deyi Li
Affiliations:
School of Remote Sensing Information Engineering, Wuhan University, Wuhan, 430079, China;School of Remote Sensing Information Engineering, Wuhan University, Wuhan, 430079, China;Bahee International, Pleasant Hill, CA 94523, USA;Beijing Institute of Electronic System Engineering, Beijing, 100039, China
Venue:
Computers & Mathematics with Applications
Year:
2011

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Abstract

Both the cloud model and type-2 fuzzy sets deal with the uncertainty of membership which traditional type-1 fuzzy sets do not consider. Type-2 fuzzy sets consider the fuzziness of the membership degrees. The cloud model considers fuzziness, randomness, and the association between them. Based on the cloud model, the paper proposes an image segmentation approach which considers the fuzziness and randomness in histogram analysis. For the proposed method, first, the image histogram is generated. Second, the histogram is transformed into discrete concepts expressed by cloud models. Finally, the image is segmented into corresponding regions based on these cloud models. Segmentation experiments by images with bimodal and multimodal histograms are used to compare the proposed method with some related segmentation methods, including Otsu threshold, type-2 fuzzy threshold, fuzzy C-means clustering, and Gaussian mixture models. The comparison experiments validate the proposed method.