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Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images. [ABSTRACT FROM AUTHOR]

Superpixel-based segmentation has been widely used as a primary prepossessing step to simplify the subsequent image processing tasks. Since determining the number of clusters is subjective and varies based on the type of image, the segmentation algorithm may provide over-segmented or under-segmented superpixels. This paper proposes an image segmentation method to improve the SLIC superpixel by region merging. It aims to improve the segmentation accuracy without defining a precise number of superls. The color difference between superpixels is employed as a homogeneity criterion for the merging process. The Berkeley dataset is used with different quantitative performance metrics to evaluate the proposed model's performance. Results obtained from probabilistic rand index (PRI), boundary recall, and under-segmentation error proved the ability of the proposed algorithm to provide comparable segmentation with a reduced number of clusters. [ABSTRACT FROM AUTHOR]

In this paper, machine learning (ML) techniques are applied at an early stage of Image Processing (IP). The learning procedures are usually applied from at least the image segmentation level, whereas, in this paper, this is done from a lower processing level: the edge detection level (ED). The main objective is to solve the edge detection problem through ML techniques. The proposed methodology is based on a classification of edges made pixel by pixel, but the predictors employed for the ML task include information about the pixel neighborhood and structures of connected pixels called edge segments. The Sobel operator is employed as input. Making use of 50 images that belong to the Berkeley Computer Vision data set, the average performance of the validation sets when employing our Neural Networks method reached an F-measure significatively higher than with the Sobel operator. The experiment results show that our post-processing technique is a promising new approach for ED. [ABSTRACT FROM AUTHOR]

To solve the problems of image threshold segmentation based on weak continuous constraint theory, the running time is long, and the two parameters need to be selected manually, and therefore a fast single-parameter energy function thresholding for image segmentation based on region information (FSEFTISRI) is proposed in this paper. The proposed FSEFTISRI algorithm uses simple linear iterative clustering (SLIC) technology to pre-block the image, extract the image super-pixels, and then map the image super-pixels to the interval type-2 fuzzy set (IT2FS), so as to construct the single-parameter energy function to search the optimal threshold, and adaptively select the penalty parameters in the energy function through the class uncertainty theory. On a non-destructive testing (NDT) database and Berkeley segmentation datasets and benchmarks (BSDS), the proposed FSEFTISRI is compared with five related algorithms. The average misclassification error (ME) of the proposed FSEFTISRI algorithm on NDT and BSDS are 0.0466 and 0.0039, respectively. The results show that the proposed FSEFTISRI has acquired more satisfactory results in visual effect and evaluation index, and the running time of the proposed FSEFTISRI algorithm is shorter, which shows the effectiveness of the proposed FSEFTISRI. [ABSTRACT FROM AUTHOR]

Image segmentation is an important part of image understanding and one of the most difficult problems in image processing. For image segmentation processing, this paper proposes an image segmentation algorithm for multilevel thresholding based on novelty harmony search algorithm. Firstly, the central harmony and central congestion distance are introduced to reduce local aggregation of initial points and expand the search range. Secondly, the new harmony generation strategy is constructed, which is based on dominant harmony learning experience. Then the search space constraints and parameters adaptive adjustment are adopted to improve the search efficiency. Finally, the harmony memory updating rules are designed to enhance the diversity of population. The image segmentation effect is evaluated by the between-class variance, peak signal-to-noise ratio and mean structural similarity. A series of experiments have been carried out to analyze the segmentation effect of the proposed NHS algorithm based on the Berkeley segmentation database. Compared with the basic harmony search algorithm, improved harmony search algorithm, global best harmony search algorithm, particle swarm optimization algorithm and artificial bee colony algorithm, the experimental results show the effectiveness of the proposed algorithm. In particular the proposed algorithm is superior to other methods when the threshold number increases. The influence of noise and artifact on image segmentation is also discussed and analyzed. It illustrates that the image can be segmented in the Gaussian noise, mixed noise and strip line artifact conditions based on the proposed algorithm. [ABSTRACT FROM AUTHOR]

Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signal–noise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23 dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. [ABSTRACT FROM AUTHOR]

The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, and traffic surveillance, etc. depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on good segmentation through multi-level thresholding. In this research, the algorithm includes two modules related to Entropy and variance. The first module is concerned with the modified firefly algorithm (FA) with Kapur's, Tsallis, and Fuzzy Entropy. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The second module is derived from the principle of variance between two classes known as between variance or inter-cluster variance. The opposition-based the learning method is used for initializing the population of candidate solutions and levying flight and local search is implemented with FA. The various experiments have been performed on Berkeley and benchmark images with distinct threshold (i.e. 2, 3, 4, 5) values. The proposed algorithm has been estimated and compared with known metaheuristic optimization methods like particle swarm optimization (PSO) and electromagnetism optimization (EMO). The results have been assessed quantitatively and qualitatively by using parameters like Peak signal-to-noise ratio (PSNR), structured similarity index metric (SSIM), objective function values, and convergence curve. The algorithm proposed observed better experiment results than PSO, EMO in terms of persistency and quality. [ABSTRACT FROM AUTHOR]

This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results. [ABSTRACT FROM AUTHOR]

Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI). The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogram-based k-means, and subtractive k-means algorithms in terms of image segmentation quality (Q-value), computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value. [ABSTRACT FROM AUTHOR]

Considering that the 3D pulse-coupled neural network (3D-PCNN) model has the deficiency of high parameter complexity and low accuracy in color image segmentation, swarm intelligence optimization algorithm is adopted to optimize the image segmentation process. In this paper, whale optimization algorithm (WOA) is adopted to optimize the 3D-PCNN model parameters E and β. The improved product cross entropy (IPCE) is chosen as the fitness function of optimization algorithm. WOA algorithm is used to find the minimum fitness function, and the corresponding optimal parameters are obtained. Through the study of image segmentation in the image segmentation library of University of Berkeley and the actual plant canopy image, the maximum entropy value and the Tsallis entropy value are compared and analyzed. Experimental results illustrate that the proposed algorithm can obtain more accurate image segmentation effect and higher segmentation rate. [ABSTRACT FROM AUTHOR]

In recent decades, image segmentation has aroused great interest of many researchers, and has become an important part of machine learning, pattern recognition, and computer vision. Among many methods of image segmentation, fuzzy C‐means (FCM) algorithm is undoubtedly a milestone in unsupervised method. With the further study of FCM, various different kinds of FCM algorithms are put forward to deal with the specific problems in image segmentation. Because there exist uncertainties in different regions of the image and similarity in the same region, reducing the uncertainty is still the main problem in image segmentation. Considering that Pythagorean fuzzy set (PFS) is a powerful tool to deal with uncertainty, in this paper, we use PFS to describe the uncertainty of image segmentation, including introducing fuzzification and defuzzification process and Pythagorean fuzzy element to describe the membership degree of pixel, combine the neighborhood information with weights and Pythagorean fuzzy distance, and propose Pythagorean fuzzy C‐means (PFCM) algorithm. Finally, we apply PFCM algorithm in image segmentation, such as different size images and Berkeley Segmentation Data Set to illustrate the effectiveness and applicability of our proposed algorithm. Meanwhile, we do comparison analysis between PFCM, fully convolution network and Deep‐image‐Prior networks, these results show that our proposed PFCM has good intuition and effectiveness. [ABSTRACT FROM AUTHOR]

• In spite of its interesting results, Mean shift (MS) has a computational cost which is prohibited for several applications. • In this paper, a new competitive segmentation algorithm for grayscale images is introduced. • To reduce the computational cost, MS is operated using a representative set of elements with a very limited number of points. • Once obtained the MS results, they are generalized to include the not involved data. • Experimental results confirm that the proposed scheme presents better performance. Multi-feature segmentation has demonstrated its superiority against one-dimensional feature approaches based on only grayscale information. Mean shift (MS) is an algorithm that has been used commonly for multi-feature segmentation. In spite of its interesting results, MS maintains a computational cost that is prohibitive for segmentation scenarios where the feature map consists of multi-dimensional features. In this paper, a new competitive segmentation algorithm for grayscale images is introduced. The proposed approach considers a two-dimensional feature map that includes the grayscale value and the local variance for each pixel in the image. To reduce the computational cost, the Mean shift (MS) algorithm is modified to operate with a very limited number of points from all available data. Under such conditions, two sets of elements are differentiated: involved data (the reduced dataset considered in the MS operation) and not involved data (the rest of the available data). Different from the classical MS, which employs Gaussian functions, in our approach, the process of estimating the feature map is carried out using a more accurate approach such as the Epanechnikov kernel function. Once the MS results are obtained, they are generalized to include the not involved data. Therefore, each unused element is assigned to the same cluster of the closest used data. Finally, clusters with the fewest elements are fused with other neighboring clusters. The proposed segmentation method has been compared with other state-of-art algorithms considering the full number of images from the Berkeley dataset. Experimental results confirm that the proposed scheme produces segmented images with a 50% better quality of visual perception approximately two times (≈ 1.8 − 2) faster than its competitors. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the key steps of target recognition. However, the accuracy of image segmentation is still challenging. To solve this problem, an image segmentation algorithm combining Pulse Coupled Neural Network (PCNN) and adaptive glowworm algorithm is proposed. The algorithm retains the advantages of the glowworm algorithm. Introduce the adaptive moving step size and the population optimal value as adjustment factors. Enhance the ability to solve the global optimal value of the fitness function. Take the weighted sum of the cross entropy, information entropy and compactness of the image as the fitness function of the glowworm algorithm. This function can ensure the visual effect of image segmentation and limit the running time while maintaining as much as possible the original information of the image. In order to intuitively evaluate the effect of the segmented image, use a number of segmentation evaluation parameters to quantify the image. Maintain the diversity of image features and improving the accuracy of image segmentation. Experimental results show that compared with other algorithms, the segmented image obtained by this algorithm has better visual effect and the segmentation performance has the best comprehensive performance. For the seven gray-scale images in the Berkeley segmentation dataset, the segmentation effect is improved by 10.85% compared with two-dimensional entropies(TDE), 9.22% compared with Genetic Algorithm(GA), and 22.58% compared with AUTO algorithm. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation is one of the crucial techniques in image processing. Even though numerous methods have been proposed in literature, it is still a challenge for the existing methods to produce steady satisfactory thresholds at manageable computational cost in segmenting images with various unknown properties. In this paper, a non-revisiting quantum-behaved particle swarm optimization (NrQPSO) algorithm is proposed to find the optimal multilevel thresholds for gray-level images. The proposed NrQPSO uses the non-revisiting scheme to avoid the re-evaluation of the evaluated solution candidates. To reduce the unnecessary computation cost, the NrQPSO provides an automatic stopping mechanism which is capable of gauging the progress of exploration and stops the algorithm rationally in a natural manner. For further improving the computation efficiency, the NrQPSO employs a meticulous solution search method to overcome the drawback of the existing QPSO algorithms using the original search methods. Performance of the NrQPSO is tested on the Berkeley segmentation data set. The experimental results have demonstrated that the NrQPSO can outperform the other state-of-the-art population-based thresholding methods in terms of efficiency, effectiveness and robustness; thus, the NrQPSO can be applied in real-time massive image processing. [ABSTRACT FROM AUTHOR]

The gray-level co-occurrence matrix (GLCM) can obtain the pixel matrix of the image, and selecting multiple thresholds for the matrix can obtain better segmentation results. However, as the number of threshold increases, the computational complexity of the algorithm will also increase. In order to solve this problem, this paper proposes a multi-threshold image segmentation method based on thermal exchange optimization (TEO) algorithm, and take a novel diagonal class entropy (DCE) as the fitness function. We improve TEO algorithm by using two strategic methods of Levy flight (LF) and opposition-based learning (OBL). In order to verify the segmentation ability of the proposed algorithm, color natural images, satellite images and Berkeley images are taken as experimental objects to analyze the segmentation result graph and image segmentation quality evaluation indexes. Experimental results show that the GLCM-ITEO algorithm has good segmentation capability, less CPU time. [ABSTRACT FROM AUTHOR]

Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works. [ABSTRACT FROM AUTHOR]

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation. [ABSTRACT FROM AUTHOR]

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model. Although the Gaussian mixture model enhances the flexibility of image segmentation, it does not reflect spatial information and is sensitive to the segmentation parameter. In this study, we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model (GMM) without parameter estimation. The proposed model highlights the residual region with considerable information and constructs color saliency. Second, we incorporate the content-based color saliency as spatial information in the Gaussian mixture model. The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria. Finally, the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation. A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art, facilitating both analytical and aesthetic objectives. For experiments, we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset. In the study, the proposed model showcases notable advancements in unsupervised image segmentation, with probabilistic rand index (PRI) values reaching 0.80, BDE scores as low as 12.25 and 12.02, compactness variations at 0.59 and 0.7, and variation of information (VI) reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets, respectively, outperforming current leading-edge methods and yielding more precise segmentations. [ABSTRACT FROM AUTHOR]

While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level for training is nearly impossible. Therefore, the study of unsupervised image segmentation methods is essential. In this paper, we present a pixel-level clustering framework for segmenting images into regions without using ground truth annotations. The proposed framework includes feature embedding modules with an attention mechanism, a feature statistics computing module, image reconstruction, and superpixel segmentation to achieve accurate unsupervised segmentation. Additionally, we propose a training strategy that utilizes intra-consistency within each superpixel, inter-similarity/dissimilarity between neighboring superpixels, and structural similarity between images. To avoid potential over-segmentation caused by superpixel-based losses, we also propose a post-processing method. Furthermore, we present an extension of the proposed method for unsupervised semantic segmentation. We conducted experiments on three publicly available datasets (Berkeley segmentation dataset, PASCAL VOC 2012 dataset, and COCO-Stuff dataset) to demonstrate the effectiveness of the proposed framework. The experimental results show that the proposed framework outperforms previous state-of-the-art methods. [Display omitted] [ABSTRACT FROM AUTHOR]

This study utilises a deep convolutional neural network (CNN) implementing regularisation and batch normalisation for the removal of mixed, random, impulse, and Gaussian noise of various levels from digital images. This deep CNN achieves minimal loss of detail and yet yields an optimal estimation of structural metrics when dealing with both known and unknown noise mixtures. Moreover, a comprehensive comparison of denoising filters through the use of different structural metrics is provided to highlight the merits of the proposed approach. Optimal denoising results were obtained by using a 20-layer network with 40 × 40 patches trained on 400 180 × 180 images from the Berkeley segmentation data set (BSD) and tested on the BSD100 data set and an additional 12 images of general interest to the research community. The comparative results provide credence to the merits of the proposed filter and the comprehensive assessment of results highlights the novelty and performance of this CNN-based approach. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the computational complexity and low segmentation precision of the multi-threshold image segmentation method, this paper proposed an ISCA based multi-threshold image segmentation method. Firstly, this method used a chaotic initialization technique to improve the quality of initial population. Secondly, it introduced an adaptive strategy to adjust the parameters according to the fitness values. Finally, it utilized an opposition-based learning strategy, then selected the better particles. The results of the Berkeley image and the plant canopy image segmentation experiments show that this method has a satisfied performance in terms of running time and segmentation accuracy. And it has a strong robustness. [ABSTRACT FROM AUTHOR]

In this paper we introduce a method for color image segmentation by computing automatically the number of clusters the data, pixels, are divided into using fuzzy c-means. In several works the number of clusters is defined by the user. In other ones the number of clusters is computed by obtaining the number of dominant colors, which is determined with unsupervised neural networks (NN) trained with the image's colors; the number of dominant colors is defined by the number of the most activated neurons. The drawbacks with this approach are as follows: (1) The NN must be trained every time a new image is given and (2) despite employing different color spaces, the intensity data of colors are used, so the undesired effects of non-uniform illumination may affect computing the number of dominant colors. Our proposal consists in processing the images with an unsupervised NN trained previously with chromaticity samples of different colors; the number of the neurons with the highest activation occurrences defines the number of clusters the image is segmented. By training the NN with chromatic data of colors it can be employed to process any image without training it again, and our approach is, to some extent, robust to non-uniform illumination. We perform experiments with the images of the Berkeley segmentation database, using competitive NN and self-organizing maps; we compute and compare the quantitative evaluation of the segmented images obtained with related works using the probabilistic random index and variation of information metrics. [ABSTRACT FROM AUTHOR]

Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called ASCQPHGS. The proposed method uses the superpixel algorithm to segment the given image, then the Density Peaks clustering (DPC) is employed to the results obtained from the superpixel algorithm to produce a decision graph. The Hunger games search (HGS) algorithm is employed as a clustering method to segment the image. The proposed method is evaluated using two different datasets, collected form Berkeley segmentation dataset and benchmark (BSDS500) and standford background dataset (SBD). More so, the proposed method is compared to several methods to verify its performance and efficiency. Overall, the proposed method showed significant performance and it outperformed all compared methods using well-known performance metrics. [ABSTRACT FROM AUTHOR]

• We present PSCT new data-driven sampling method based clustering algorithms. • PSCT output can be used for image segmentation and image reconstruction. • Image reconstructed with PSCT show similar performance than original images in our tests. In this paper, we describe Pixel Sampling Clustering Technique (PSCT), a data-driven sampling procedure used to reduce pixel sets. We view the pixels in an image as a high redundancy 3D space. We also refer to this space as our color model. Our method aims to retain a relevant sample of the data so it can act as a new smaller, hence more efficient, color model. PSCT uses a pair of fast density-based clustering algorithms in tandem. First, it applies Birch and then DBSCAN to keep the most densely represented colors. We cluster the resulting color model and use the labels to segment images. We also complement the sampling method with a refinement algorithm intended to improve color representation. In our paper, we show how to reconstruct images using our reduced color model. We also show that reconstructed images have enough information to perform image related learning tasks with almost the same accuracy than the original images but with only a small fraction of the data. We test our sampling method in three image related supervised and unsupervised tasks and compare them with state-of-the-art methods. For our experiments, we use two image datasets: MIT's Vision Texture Dataset and Berkeley's BSD500. [ABSTRACT FROM AUTHOR]

• BLS is employed as the surrogate model to assist the evolutionary process. • Fuzzy clustering functions with image region information are constructed. • An adaptively updating strategy for the parameters of BLS is adopted. • A model update mechanism is used to boost the prediction accuracy of BLS. For some real-world problems, the objective function evaluation is time-consuming and computationally expensive in multi-objective evolutionary algorithms. Surrogate-assistance approaches can be used to estimate the objective function and solve the computationally expensive problem. Taking advantages of the flatted structure and incremental learning, broad learning system (BLS) has shown a great improvement of time efficiency on the premise of ensuring the performance on the classification and regression problems. In this paper, BLS is adopted to design a BLS surrogate-assisted evolutionary framework to boost the time efficiency of clustering-based image segmentation. A BLS-assisted multi-objective evolutionary fuzzy clustering algorithm with reference points (BLS-MOEFC) is proposed. Firstly, two fuzzy clustering objective functions integrating image region information are constructed as the optimized fitness functions for obtaining satisfactory segmentation quality. Secondly, in order to improve the optimization efficiency and reduce the computational time cost, a BLS surrogate model-assisted multi-objective evolutionary framework with reference points is designed to optimize the constructed objective functions. Thirdly, an adaptively updating strategy of parameters and a model update mechanism based on the non-dominated sorting are adopted to improve the prediction accuracy of the BLS surrogate model. Finally, a novel fuzzy clustering validity index integrating region information is designed to select an optimal solution from the final set of non-dominated solutions. Experimental results on Berkeley and Weizmann images show that the proposed algorithm behaves well in the segmentation performance and time cost. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of classification of object and background in unevenly illuminated images using Decision-Theoretic Rough Set (DTRS) framework. The proposed scheme employs adaptive windowing technique to partition the image into different windows. Thereafter, the proposed DTRS based method is applied on each window to find out the optimal threshold that is used for classification of the window. Determination of optimal threshold of a given window is dependent on the optimal granule size used for the window. The problem of determination of optimal granule size and optimal threshold is cast in optimization framework. The optimal threshold obtained for each window is used to classify the window and the classification of the entire image is the union of classifications over all the windows. Manual tuning of parameters is not required to determine the optimal threshold. The proposed scheme is tested on different images considered from Berkeley image database. The performance of the proposed scheme is compared with other granular and non-granular computing based schemes. Evaluation of different quantitative measures demonstrates the improved performance of the proposed schemes over others. • Image classification under uneven illumination conditions. • Adaptive windowing strategy to generate different windows over the image. • Parameterless classification of windows of an image using granular computing. • Automatic determination of optimal granule size. • Simultaneous determination of optimal threshold for image classification. [ABSTRACT FROM AUTHOR]

The fuzzy c-means (FCM) algorithm is a popular method for data clustering and image segmentation. However, the main problem of this algorithm is that it is very sensitive to the initialization of primary clusters, so it may not perform well in segmenting complex images. Another problem with the FCM is the equal importance of the image features used during the segmentation process, which causes unstable performance on different images. In this paper, we propose an FCM-based color image segmentation approach, termed CGFFCM, applying an automatic cluster weighting scheme to reduce the sensitivity to the initialization, and a group-local feature weighting strategy to better image segmentation. Also, we combine the proposed clustering algorithm with the Imperialist Competitive Algorithm (ICA) to optimize the feature weighting process. In addition, we apply an efficient combination of image features to increase the segmentation quality. The performance of CGFFCM is evaluated and compared with state-of-the-art methods (such as SMKIFC (semi-supervised surrogate-assisted multi-objective kernel intuitionistic fuzzy clustering), and A-PSO-IT2IFCM (alternate particle swarm optimization based adaptive interval type-2 intuitionistic FCM clustering algorithm)) using the Berkeley benchmark dataset. The results obtained by CGFFCM are 95%, 79%, and 91%, in terms of average Accuracy , NMI , and F-score metrics, respectively, which all are better than the competitors. The implementation source code of CGFFCM is made publicly available at https://github.com/Amin-Golzari-Oskouei/CGFFCM. • A cluster weighting scheme to reduce the sensitivity of the image segmentation to the selection of initial centers. • A group-local feature weighting strategy to measure the importance and weights of image features precisely. • An automatic way to calculate the cluster and feature weights simultaneously during the learning process. • A combination of ICA meta-heuristic algorithm with proposed clustering algorithm to find the optimal group feature weights. [ABSTRACT FROM AUTHOR]

• An efficient method for image thresholding is proposed. • The Harris hawk optimizer is used for image segmentation. • The Harris hawk optimizer is tested over a multidimensional real problem. • The quality of the segmentation results is higher than other algorithms. • The performance of the algorithm is tested on digital mammograms. Segmentation is a crucial phase in image processing because it simplifies the representation of an image and facilitates its analysis. The multilevel thresholding method is more efficient for segmenting digital mammograms compared to the classic bi-level thresholding since it uses a higher number of intensities to represent different regions in the image. In the literature, there are different techniques for multilevel segmentation; however, most of these approaches do not obtain good segmented images. In addition, they are computationally expensive. Recently, statistical criteria such as Otsu, Kapur, and cross-entropy have been utilized in combination with evolutionary and swarm-based strategies to investigate the optimal threshold values for multilevel segmentation. In this paper, an efficient methodology for multilevel segmentation is proposed using the Harris Hawks Optimization (HHO) algorithm and the minimum cross-entropy as a fitness function. To substantiate the results and effectiveness of the HHO-based method, it has been tested over a benchmark set of reference images, with the Berkeley segmentation database, and with medical images of digital mammography. The proposed HHO-based solver is verified based on other comparable optimizers and two machine learning algorithms K -means and the Fuzzy IterAg. The comparisons were performed based on three groups. This first one is to provide evidence of the optimization capabilities of the HHO using the Wilcoxon test, and the second is to verify segmented image quality using the PSNR, SSIM, and FSIM metrics. Then, the third way is to verify the segmented image comparing it with the ground-truth through the metrics PRI, GCE, and VoI. The experimental results, which are validated by statistical analysis, show that the introduced method produces efficient and reliable results in terms of quality, consistency, and accuracy in comparison with the other methods. This HHO-based method presents an improvement over other segmentation approaches that are currently used in the literature. [ABSTRACT FROM AUTHOR]

In the task of interactive image segmentation, the user initially indicates some pixels as target seeds and background seeds and propagates the seeds' labels to the rest of the image. We propose an equidistant seed generation method for interactive image segmentation that combines saliency detection and random walk (RW) with restart with optimal restarting probability. Our method includes two stages: first, we utilize the saliency detection method to obtain the initial target region and generate the initial seeds. Then we relabel the seeds using a Gaussian mixture model with limited user interaction to obtain accurate seeds. The relabeled seeds can limit the range of object; therefore, in the second stage, we can select an optimal restarting probability by calculating the area of the RW that exceeds the limited range of the object seeds. Our method can generate effective seeds and thus reduce the dependence on user interaction. Furthermore, using the optimal restarting probability, we can obtain higher segmentation accuracy. Extensive experiments on the Berkeley segmentation dataset, GrabCut dataset, and MSRA1000 dataset demonstrate that our method can reduce the dependence on user interaction and achieve better performance than the latest interactive image segmentation methods, such as RW, RW with restart, normalized RW, sub-Markov RW, and label propagation through complex networks. [ABSTRACT FROM AUTHOR]