Gray Scale Image Multi-Thresholding with Chaotic Cuckoo Search

Varsha, Saxena; Rajinikanth, V.; Prabhu, G. Ananth

doi:10.1109/icscan.2018.8541262

Cited by 4 publications

(1 citation statement)

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“…The proposed method showed better performance than several state-of-arts methods such as PSO, FA, and bacterial foraging optimization (BFO). Varsha et al [43]presented a new MTS method based on chaotic cuckoo search (CCS) and Tsallis entropy. Moreover, Cuckoo Search algorithm has been successfully applied for both gary-scale images [44] and color images [44], [45] and it assesses its quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation

et al. 2020

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Multilevel-thresholding is an efficient method used in image segmentation. This paper presents a hybrid meta-heuristic approach for multi-level thresholding image segmentation by integrating both the artificial bee colony (ABC) algorithm and the sine-cosine algorithm (SCA). The proposed algorithm, called ABCSCA, is applied to segment images and it utilizes Otsu's function as the objective function. The proposed ABCSCA uses ABC to optimize the threshold and to reduce the search region. Thereafter, the SCA algorithm uses the output of ABC to determine the global optimal solution, which represents the thresholding values. To evaluate the performance of the proposed ABCSCA, a set of experimental series is performed using nineteen images. In the first experimental series, the proposed ABCSCA is assessed at the low threshold levels and compared with the ABC and SCA as traditional methods. Moreover, the second experimental series aims to evaluate the ABCSCA at high threshold levels and it is compared with six algorithms in addition to the SCA and ABC. Besides, the proposed method is evaluated using the fuzzy entropy. The results demonstrate the effectiveness of the proposed algorithm and showed that it outperforms other algorithms in terms of performance measures, such as Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM).INDEX TERMS Image segmentation, multi-level thresholding, artificial bee colony (ABC), sine-cosine algorithm (SCA).

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Section: Literature Reviewmentioning

confidence: 99%