A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve

Display OmittedA complete flowchart routine of energy curve based multilevel image thresholding. Energy curve based colour multilevel thresholding has been proposed.CS and ELR-CS based optimization techniques have been exploited.Different objective functions have been utilized for optimum results.CS-Kapur's found giving better results. Amongst all the multilevel thresholding techniques, standard histogram based thresholding approaches are very impressive for bi-level thresholding. But, it is not effective to select spatial contextual information of the image for choosing optimal thresholds. In this paper, a new color image thresholding technique is presented by using an energy function to generate the energy curve of an image by considering spatial contextual information of the image. The property of this energy curve is very much similar to histogram of the image. To estimate the spatial contextual information for thresholding practice, in place of histogram, the energy curve function is used as an input. A new energy curve based color image segmentation approach using three well known objective functions named Kapur's entropy, between-class-variance, and Tsalli's entropy is proposed. In this paper, cuckoo search (CS) and egg lying radius-cuckoo search (ELR-CS) optimization algorithms with different parameter analysis have been used for solving the color image multilevel thresholding problem. The experimental results demonstrate that the proposed CS-Kapur's energy curve based segmentation can powerfully and accurately search the multilevel thresholds.

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