A new hybrid image segmentation approach using clustering and black hole algorithm. (1st March 2020)

• Record Type: Journal Article
• Authors: Dhanachandra, Nameirakpam; Chanu, Y. Jina; Singh, Kh. Manglem
• Is Part Of: Computational intelligence. Volume 39:Number 2(2023)
• Page Start: 194
• Page End: 213
• Publication Date: 2020-03-01
• Subjects: black hole algorithm -- clustering -- image segmentation -- region merging; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12297
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Clustering technique is used in image segmentation because of its simple and easy approach. However, the existing clustering techniques required prior information as input and the performance are entirely dependent on this prior information, which is the main drawback of the clustering approaches. Therefore, many researchers are trying to introduce a novel method with user free parameter. We proposed a clustering method, that is, independent of user parameters and later we used a region merging technique to improve the performance of the clustering output. In this article, we proposed a hybrid image segmentation method which is based on a clustering algorithm and black hole algorithm. In the clustering technique, we have used recursive density estimation technique of surrounding pixels. After clustering technique, presence of small segments may be present and it would give lower a performance of segmentation output. Therefore, a segment is merged with another segment by finding best matched segment. Black hole algorithm concept has been used to define the fitness of each segment and to find the best matching segment. We have compared the proposed method with the other clustering‐based segmentation methods and different evaluation indices are used to calculate the performance, and the result proved the effectiveness of the proposed algorithm.