Advancements in Fuzzy Clustering Algorithms for Image Processing: A Comprehensive Review and Future Directions

Vatsala Anand (), Deepika Koundal (), Thongchai Surinwarangkoon () and Kittikhun Meethongjan ()
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Vatsala Anand: Chitkara University, Chitkara University Institute of Engineering and Technology
Deepika Koundal: University of Eastern Finland
Thongchai Surinwarangkoon: Suan Sunandha Rajabhat University, Department of Computer Business, College of Innovation and Management
Kittikhun Meethongjan: Suan Sunandha Rajabhat University, Department of Apply Science, Faculty of Science and Technology

Chapter Chapter 11 in Advances in Data Clustering, 2024, pp 201-217 from Springer

Abstract: Abstract Fuzzy clustering algorithms have emerged as powerful tools for various image processing tasks, owing to their ability to handle uncertainties and ambiguities inherent in image data. This chapter provides a comprehensive review of recent advancements in fuzzy clustering algorithms for image processing, focusing on applications such as image classification, texture analysis, segmentation of remote sensing images, and object recognition. Specifically, we discuss the principles and applications of fuzzy clustering in image classification, texture analysis, and segmentation tasks, highlighting the advantages and limitations of popular algorithms such as fuzzy C-means (FCM), spatial fuzzy C-means (SFCM), and intuitionistic fuzzy C-means (IFCM). Furthermore, we present a comparative analysis of these algorithms based on their performance metrics and suitability for different image processing tasks. Finally, we identify open challenges and propose potential future research directions in fuzzy clustering for image processing, including handling high-dimensional data, integration with deep learning techniques, scalability, interpretability, and addressing complex image structures.

Keywords: Fuzzy clustering; Image processing; Image segmentation; Texture analysis; Fuzzy C-Means (FCM); Information and communication technology; Scientific research; Technology Spatial Fuzzy C-Means (SFCM); Research development; Intuitionistic Fuzzy C-Means (IFCM); Comparative analysis; Future directions (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-7679-5_11

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