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Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and unstable exploitation and exploration. To overcome these, an improved SSA called as adaptive salp swarm algorithm (ASSA) was proposed. Thresholding is among the most effective image segmentation methods in which the objective function is described in relation of threshold values and their position in the histogram. Only if one threshold is assumed, a segmented image of two groups is obtained. But on other side, several groups in the output image are generated with multilevel thresholds. The methods proposed by authors previously were traditional measures to identify objective functions. However, the basic challenge with thresholding methods is defining the threshold numbers that the individual must choose. In this paper, ASSA, along with type II fuzzy entropy, is proposed. The technique presented is examined in context with multilevel image thresholding, specifically with ASSA. For this reason, the proposed method is tested using various images simultaneously with histograms. For evaluating the performance efficiency of the proposed method, the results are compared, and robustness is tested with the efficiency of the proposed method to multilevel segmentation of image; numerous images are utilized arbitrarily from datasets. [ABSTRACT FROM AUTHOR]

• A color image segmentation based on Exchange Market Algorithm (EMA) is proposed. • Between class variance and entropy functions are used as objective functions. • The proposed EMA approach is compared with other state-of-the-art algorithms. • Results reveal that the EMA is the effective approach for multilevel thresholding. Color image segmentation is the primary step to elicit detailed information of the image with RGB color space. The key to acquire the region of interest is the straightforward, instinctual technique called thresholding. Bi-level thresholding can be assessed concretely for simple images, whereas revealing various classes from a complex image is achieved only through multiple threshold values. Hence, multilevel thresholding with the most propitious maximizing objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE) to extract the precise threshold are employed in this paper. As the segmentation level increases, execution of non-parametric objective functions leads to exponential increase in computational time. Several researches to enhance the speed of objective functions along with various metaheuristics such as Krill Herd Algorithm (KHA), Teaching-Learning Based Optimization (TLBO) and Cuckoo Search Algorithm (CSA) are performed. The need to unlock the computational complexity drifted our view towards the most powerful, robust, recently introduced metaheuristic Exchange Market Algorithm (EMA). EMA involves exchange of shares among investors to gain profit. The strategic approach of share members in stable and unstable modes of the market results in precise exploration and exploitation. Furthermore, low fitness shareholders gaining experience from their high and moderate counterparts, stands out as an exceptional step to reduce the time consumption. This concept is utilized in our paper for the first time to obtain the exact details from the image without any delay. Empirical outcome of the results indicates that exceptional image segmentation is achieved by EMA in less time compared to extensive search techniques such as KHA, TLBO and CSA. Quantitative and qualitative validation furnished by metrics such as Structural Similarity Index (SSIM), computational time, Peak Signal to Noise Ratio (PSNR) and statistical Wilcoxon test affirm that Otsu, Kapur and MCE based EMA outperforms the existing techniques to analyze the real-world images. [ABSTRACT FROM AUTHOR]

Infrared pedestrian segmentation is a challenging problem in infrared image processing. Kaniadakis entropy thresholding method can segment the infrared image with long tail distribution histogram. However, it failed in images with noise and complex background. In this paper, a novel infrared pedestrian segmentation algorithm based on the two-dimensional Kaniadakis entropy thresholding is proposed to address this problem. First, in order to introduce the spatial information of pixels, the two-dimensional Kaniadakis entropy thresholding algorithm is proposed through extending the one-dimensional Kaniadakis entropy thresholding algorithm and using the two-dimensional histogram of image. The recursive formulas of the two-dimensional algorithm are also presented to improve the computation efficiency. Second, an intensity suppressed strategy is embedded in the optimal thresholds searching process of the two-dimensional Kaniadakis entropy thresholding algorithm to segment the pedestrian in infrared images with complex background. In the experiment, the proposed algorithm is compared with the state-of-the-art image segmentation methods on several infrared images with different backgrounds. The experimental results verify the effectiveness of the proposed method both qualitatively and quantitatively. • Extend the Kaniadakis entropy thresholding to 2d. • Derive the recursive formulas of the 2d Kaniadakis entropy thresholding. • Propose a suppressed strategy in 2d-Kan for infrared pedestrian segmentation. • Verify the effectiveness of the proposed infrared pedestrian segmentation algorithm. [ABSTRACT FROM AUTHOR]

The digital image segmentation is an open problem that is growing day by day and is attracting the attention of researchers from last few years. Image resolution and their speed has led to the use of thresholding approaches. Image thresholding is simple, easy and effective method for image segmentation. Multi-level image thresholding is a key perspective in several real-time pattern recognition and image processing-based applications. It identifies pixels quickly and effectively in different groups indicating multiple regions in an image. Segmentation of images based on thresholding by using various intelligent optimization techniques with fuzzy entropy is widely utilized for defining thresholds in a better way to use them precisely. In this research, a novel technique for multi-level thresholding is proposed by combining Fuzzy Entropy Type II (FE-TII) with recently developed meta-heuristics named Marine Predators Algorithm (MPA). For achieving optimal thresholds of an image, the maximization of entropy is tedious and consumes a lot of time with an increasing number of thresholds. The MPA method presented is analyzed in context with image segmentation, particularly on thresholds with TII-FE. For this reason, proposed methodology is evaluated using several images along with the distribution of histograms. For analyzing the performance efficiency of the proposed methodology, the results are compared and robustness is tested with efficiency of proposed technique to multi-level image segmentation, several images are used randomly from datasets. [ABSTRACT FROM AUTHOR]

Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria. [ABSTRACT FROM AUTHOR]

Multilevel thresholding method is one of the most popular techniques in image segmentation. However, the multilevel thresholding method is time-consuming, its time complexity increases exponentially with the number of threshold levels. In this paper, in order to improve the computation efficiency of the multilevel minimum cross entropy thresholding, the iterative formula of the multilevel cross entropy thresholding algorithm is proposed and compared with the modern meta-heuristic optimization algorithms. The iterative multilevel cross entropy thresholding algorithm can find the thresholds close to the global optimums with less time. The computation cost of the iterative multilevel cross entropy thresholding algorithm is linear in the number of the threshold levels. We prove the convergence of the iterative algorithm and compare the iterative multilevel cross entropy thresholding algorithm with multilevel cross entropy thresholding methods combined with the state-of-the-art meta-heuristic optimization techniques including particle swarm optimization (PSO), cuckoo search algorithm (CS), differential evolution (DE), crow search algorithm (CSA) and genetic algorithm (GA). Experimental results show that the iterative multilevel cross entropy thresholding algorithm is efficient and effective. Therefore, iterative algorithm for the multilevel cross entropy thresholding is an effective method to improve the computation efficiency. • Propose the iterative formula of the multilevel minimum cross entropy thresholding (MMCET) method. • Prove the convergence of the iterative algorithm. • Compare the iterative algorithm with the MMCET method optimized by swarm search algorithm. • Validate the effectiveness of the iterative MMCET algorithm. [ABSTRACT FROM AUTHOR]