Multilevel thresholding of color images

Authors: Sanjay Agrawal, Rutuparna Panda, Pratiksha Choudhury and Ajith Abraham

Download link: https://authors.elsevier.com/a/1eTfL3OAb95JtI

Journal: Knowledge-Based Systems (240: 108172, 2022)

DOI: https://doi.org/10.1016/j.knosys.2022.108172

Abstract: Optimal multilevel thresholding for image segmentation got much importance in recent years. Several entropic and non-entropic objective functions with evolutionary computing algorithms have been successfully implemented to get the optimal multilevel thresholds for gray scale images. The problem of multilevel thresholding becomes complex for color images. Because, the basic color components (red, blue, green) of the color image are extracted and the multiple optimum threshold values are calculated for each of the components separately. This makes the methods computationally intensive and inaccurate. Further, the required color information is not retained in the thresholded output. To solve these problems, an efficient technique is proposed in this paper, extracting only the dominant color component (DCC) of an image, for optimal thresholding. A novel segmentation score is introduced to justify the methodology. The optimum threshold values are obtained using a newly suggested evolutionary computing technique named adaptive whale optimization algorithm (AWOA). The main contributions are – (i) a novel DCC approach is introduced, (ii) an efficient optimizer AWOA is proposed, (iii) a new segmentation score is introduced, (iv) experimental results on standard test color images are explored. The outcomes are compared with all existing method’s approaches (using all the RGB components) on color image thresholding. Its performance analysis using standard metrics is deliberated in detail. Statistical analysis is also performed. From the outcomes, it is perceived that the suggested DCC-AWOA concept yields high quality segmented images. The work may encourage further research to explore its high dimensional applications.

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