Self-sparse fuzzy clustering with automatic region merging approach for image segmentation

Abstract

Image segmentation based on fuzzy clustering has made enormous progress in recent years. However, the performance of existing methods degrades in noisy conditions and suffers from losses of foregrounds and the emergence of backgrounds. To settle these issues, in this paper, two methods – proposed method 1 and proposed method 2 – based on self-sparse fuzzy clustering algorithm (SSFCA) are proposed. The proposed methods use a normal shrink (NS) denoising algorithm as a pre-processing step for the suppression of noise. The output of NS undergoes the segmentation block SSFCA to get the segmentation output. The segmentation output is then subjected to a post-processing block for the removal of isolated pixels. In proposed method 1, the morphological cleaning operation acts as a post-processing block, whereas it is the morphological cleaning operation along with the automatic region merging approach in proposed method 2. The automatic region merging is achieved using connected component filtering based on the area density balance strategy (CCF-ADB). The proposed methods are tested on different datasets, and experimental results demonstrate their superior performance and effectiveness as compared to the state-of-art algorithms.

KEYWORDS:

• Fuzzy c-means clustering (FCM)
• image segmentation
• over-segmentation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data will be made available on request.

Additional information

Notes on contributors

Tunirani Nayak

Miss Tunirani Nayak has receiver her Btech degree from UCE, Burla and MTech Degree from ITER, SOA university, Odisha, India. Now she is pursuing her PhD at Veer Surendra Sai University of Technology, Odisha. She has more than 12 years of teaching experience in different institutes. Miss Nayak is currently working at Veer Surendra Sai University of Technology as Assistant Professor in the Department of ETC Eng. His research interest includes image processing, video processing, signal processing, computer vision, artificial intelligence and machine learning.

Nilamani Bhoi

Dr. Nilamani Bhoi has received PhD degree from National Institute of Technology, India in 2009.He has more than 13 years of teaching experience in different institutes. Dr. Bhoi is currently working at Veer Surendra Sai University of Technology as Associate Professor in the Department of ETC Eng. His research interest includes image processing, computer vision, artificial intelligence and machine learning.