Abstract
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).
Highlights
Smart systems using pattern recognition techniques have been applied in several fields in recent years, such as object identification, face recognition, and computer vision
A hybrid model called FASSO was proposed by [17], it combined the Firefly algorithm (FA) and Social Spider Optimization (SSO) algorithm and evaluated them on Multi-level thresholding segmentation (MTS) tasks, the FASSO provided a lower computational time and enhanced the searching phase of the social spider optimization (SSO) algorithm. Another method was developed by [18] called WOAPSO, it uses the whale optimization algorithm (WOA) and the Particle Swarm Optimization (PSO) to separately update the population in parallel; whereas the population is divided into two parts and each part is updated using one of the algorithms the union of both new solutions is evaluated according to the fitness function
EXPERIMENTS AND RESULTS we performed a set of experimental series to evaluate the performance of the proposed ABCSCA algorithm
Summary
Smart systems using pattern recognition techniques have been applied in several fields in recent years, such as object identification, face recognition, and computer vision. A hybrid model called FASSO was proposed by [17], it combined the FA and Social Spider Optimization (SSO) algorithm and evaluated them on MTS tasks, the FASSO provided a lower computational time and enhanced the searching phase of the SSO algorithm Another method was developed by [18] called WOAPSO, it uses the whale optimization algorithm (WOA) and the PSO to separately update the population in parallel; whereas the population is divided into two parts and each part is updated using one of the algorithms the union of both new solutions is evaluated according to the fitness function. A new segmentation technique based on the hybridization of ABC and SCA is presented The strengths of these algorithms are utilized to enhance the performance of the MTS phase.
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