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Your search keyword '"Wu, Chengmao"' showing total 9 results
Start Over Author "Wu, Chengmao" Topic local information
9 results on '"Wu, Chengmao"'

8. A novel interval-valued data driven type-2 possibilistic local information c-means clustering for land cover classification.

Author

Wu, Chengmao and Guo, Xiaokang

Subjects
*LAND cover, *REMOTE sensing, *SOFT sets, *CLASSIFICATION, *INFORMATION modeling, *FUZZY clustering technique
Abstract

In the process of land cover classification, existing fuzzy clustering is not enough to describe the high-order fuzzy uncertainty, while possibilistic clustering has serious parameter dependence and cluster consistency, which makes them unable to effectively deal with the phenomena of "different objects with the same spectrum" and "different objects with the same spectrum". Hence, this paper proposes a robust type-2 possibilistic C-means clustering with local information and interval-valued data model for remote sensing land cover classification. Firstly, according to the local neighborhood variance, remote sensing information is modeled as interval-valued data. Secondly, existing possibilistic C-means clustering is modified and an enhanced possibilistic C-means clustering is obtained. Once again, it is used to clustering interval-valued data and a single weighting exponent type-2 possibilistic C-means clustering with double distance measures is constructed. Finally, to further improve the robustness of clustering method, local information is embedded into the objective function of this enhanced type-2 possibilistic C-means clustering and a novel robust possibilistic clustering-related algorithm for remote sensing information classification is proposed. Experimental results show that the proposed algorithm outperforms existing state of the art type-2 clustering-related algorithms, and is of great significance to the interpretation of remote sensing images. [ABSTRACT FROM AUTHOR]

Published
2022
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9. Robust interval type-2 kernel-based possibilistic fuzzy clustering algorithm incorporating local and non-local information.

Author

Wu, Chengmao and Peng, Siyun

Subjects
*FUZZY algorithms, *FUZZY sets, *SOFT sets, *PIXELS, *IMAGE segmentation, *IMAGE processing, *SET theory, *PATTERN recognition systems
Abstract

• An interval type-2 possibilistic fuzzy clustering with local information is proposed. • Deep neighborhood window is used to enhance the adaptive ability of algorithm. • Local and non-local information is used to improve the robustness of algorithm. • Using structural similarity to enhance the influence of local spatial information. • Testing results show that the proposed algorithm has extremely good performance. Type-2 fuzzy set theory has certain potential advantage in processing high-order uncertainty, and has been more and more widely applied in pattern recognition, image processing, and system modeling. Although it has been introduced into robust fuzzy clustering modeling, most clustering methods based on type-2 fuzzy set theory have many obvious shortcomings, such as weak robustness against noise and high sensitivity to initial value. Therefore, this paper presents a novel single fuzzifier interval type-2 kernel-based possibilistic fuzzy local and non-local information c-means clustering, and it is driven by deep neighborhood information for image segmentation in the presence of high noise. Firstly, we construct the novel deep neighborhood window structure, which consists of local neighborhood window around current pixel and deep neighborhood window around the pixel in local neighborhood window around current pixel. Secondly, using the structural similarity between local neighborhood window and deep neighborhood window around current pixel, we construct a novel fuzzy local information factor to tune the impact of local neighborhood information on current pixel clustering. Thirdly, possibility theory and fuzzy local information factor are introduced into interval type-2 kernel-based fuzzy c-means clustering, and a novel single fuzzifier type-2 possibilistic fuzzy clustering with richer local and non-local information and kernel metric is proposed. Experimental results indicate that the proposed algorithm outperforms existing state-of-the-art robust fuzzy clustering-related algorithms, and significantly improve the robustness of robust fuzzy clustering algorithms against noise. [ABSTRACT FROM AUTHOR]

Published
2023
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