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Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [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]

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class. [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]

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]