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In this paper, in order to search the global optimum solution with a very fast convergence speed across the whole search space, we propose a partitioned and cooperative quantum-behaved particle swarm optimization (SCQPSO) algorithm. The auxiliary swarms and partitioned search space are introduced to increase the population diversity. The cooperative theory is introduced into QPSO algorithm to change the updating mode of the particles in order to guarantee that this algorithm well balances the effectiveness and simplification. Firstly, we explain how this method leads to enhanced population diversity and improved algorithm over previous strategies, and emphasize this algorithm with comparative experiments using five benchmark test functions and five shift complex functions. After that we demonstrate a reasonable application of the proposed algorithm, by showing how it can be used to optimize the parameters for OTSU image segmentation for processing medical images. The results show that the proposed SCQPSO algorithm outperforms than the other improved QPSO in terms of the quality of the solution, and performs better for solving the image segmentation than the QPSO algorithm, the sunCQPSO algorithm, the CCQPSO algorithm. [ABSTRACT FROM AUTHOR]

Clustering learning and classification learning are two major tasks in pattern recognition. The traditional hybrid clustering and classification algorithms handle them in a sequential way rather than a simultaneous way. Fortunately, multiobjective optimization provides a way to solve this problem. In this paper, an algorithm that learns simultaneous clustering and classification adaptively via multiobjective evolutionary algorithm is proposed. The main idea of this paper is to optimize two objective functions which represent fuzzy cluster connectedness and classification error rate to achieve the goal of simultaneous learning. Firstly, we adopt a graph based representation scheme to encode so that it can generate a set of solutions with different number of clusters in a single run. Then the relationship between clustering and classification is built via the Bayesian theory during the optimization process. The quality of clustering and classification is measured by the objective functions and the feedback drawn from both aspects is used to guide the mutation. At last, a set of nondominated solutions are generated, from which the final Pareto optimal solution is selected by using Adjusted Rand Index. The results on synthetic datasets and real-life datasets demonstrate the rationality and effectiveness of the proposed algorithm. Furthermore, we apply the proposed algorithm to image segmentation including texture images and synthetic aperture radar images, the experimental results show the superiority of the proposed algorithm compared with other five algorithms. [ABSTRACT FROM AUTHOR]

In order to achieve robust performance of preserving significant image details while removing noise for image segmentation, this paper presents a multi-objective evolutionary fuzzy clustering (MOEFC) algorithm to convert fuzzy clustering problems for image segmentation into multi-objective problems. The multi-objective problems are optimized by multi-objective evolutionary algorithm with decomposition. The decomposition strategy is adopted to project the multi-objective problem into a number of sub-problems. Each sub-problem represents a fuzzy clustering problem incorporating local information for image segmentation. Opposition-based learning is utilized to improve search capability of the proposed algorithm. Two problem-specific techniques, an adaptive weighted fuzzy factor and a mixed population initialization, are introduced to improve the performance of the algorithm. Experiment results on synthetic and real images illustrate that the proposed algorithm can achieve a trade-off between preserving image details and removing noise for image segmentation. [ABSTRACT FROM AUTHOR]

In this paper, a novel region-based fuzzy active contour model with kernel metric is proposed for a robust and stable image segmentation. This model can detect the boundaries precisely and work well with images in the presence of noise, outliers and low contrast. It segments an image into two regions – the object and the background by the minimization of a predefined energy function. Due to the kernel metric incorporated in the energy and the fuzziness of the energy, the active contour evolves very stably without the reinitialization for the level set function during the evolution. Here the fuzziness provides the model with a strong ability to reject local minima and the kernel metric is employed to construct a nonlinear version of energy function based on a level set framework. This new fuzzy and nonlinear version of energy function makes the updating of region centers more robust against the noise and outliers in an image. Theoretical analysis and experimental results show that the proposed model achieves a much better balance between accuracy and efficiency compared with other active contour models. [ABSTRACT FROM AUTHOR]

Based on clonal selection mechanism in immune system, a dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partition of datasets. The real based antibody encoding consists of the activation thresholds and the clustering centers. Then based on the special structures of chromosomes, a particular dynamic local search scheme is proposed to exploit the neighborhood of each antibody as much as possible so to realize automatic variation of the antibody length during evolution. The dynamic local search scheme includes four basic operations, namely, the external cluster swapping, the internal cluster swapping, the cluster addition and the cluster decrease. Moreover, a neighborhood structure based clonal mutation is adopted to further improve the performance of the algorithm. The proposed algorithm has been extensively compared with five state-of-the-art automatic clustering techniques over a suit of datasets. Experimental results indicate that the DLSIAC is superior to other five clustering algorithms on the optimum number of clusters found and the clustering accuracy. In addition, DLSIAC is applied to a real problem, namely image segmentation, with a good performance. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness. [Copyright &y& Elsevier]

Abstract: Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions (GIFP_FCM) is a novel fuzzy clustering algorithm. However when GIFP_FCM is applied to image segmentation, it is sensitive to noise in the image because of ignoring the spatial information contained in the pixels. In order to solve this problem, a novel fuzzy clustering algorithm with non local adaptive spatial constraint (FCA_NLASC) is proposed in this paper. In the proposed method, a novel non local adaptive spatial constraint term is introduced to modify the objective function of GIFP_FCM. The characteristic of this technique is that the adaptive spatial parameter for each pixel is designed to make the non local spatial information of each pixel playing a different role in guiding the noisy image segmentation. Segmentation experiments on synthetic and real images, especially magnetic resonance (MR) images, are performed to assess the performance of an FCA_NLASC in comparison with GIFP_FCM and fuzzy c-means clustering algorithms with local spatial constraint. Experimental results show that the proposed method is robust to noise in the image and more effective than the comparative algorithms. [ABSTRACT FROM AUTHOR]

Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imaging system is usually an observation of the earths' surface. It means that rich structures exist in SAR images. Convolutional neural network (CNN) is good at learning features from raw data automatically, especially the structural features. Inspired by these, we propose a novel SAR image segmentation method based on convolutional-wavelet neural networks (CWNN) and Markov Random Field (MRF). In this approach, a wavelet constrained pooling layer is designed to replace the conventional pooling in CNN. The new architecture can suppress the noise and is better at keeping the structures of the learned features, which are crucial to the segmentation tasks. CWNN produces the segmentation map by patch-by-patch scanning. The segmentation result of CWNN will be used with two labeling strategies (i.e., a superpixel approach and a MRF approach) to produce the final segmentation map. The superpixel approach is used to enforce the smooth nature on the local region. On the other hand, the MRF approach is used to preserve the edges and the details of the SAR image. Specifically, two segmentation maps will be produced by applying the superpixel and MRF approaches. The first segmentation map is obtained by combining the segmentation map of CWNN and the superpixel approach, and the second segmentation map is obtained by applying the MRF approach on the original SAR image. Afterwards, these two segmentation maps are fused by using the sketch map of the SAR image to produce the final segmentation map. Experiments on the texture images demonstrate that the CWNN is effective for the segmentation tasks. Moreover, the experiments on the real SAR images show that our approach obtains the regions with labeling consistency and preserves the edges and details at the same time. [ABSTRACT FROM AUTHOR]

Subspace segmentation based salient object detection has received increasing interests in recent years. To preserve the locality and similarity of regions, a grouping effect of representation is introduced to segment the salient object and background in subspace. Then a new saliency map is calculated by incorporating this local graph regularizer into coding, which explicitly explores the data self-representation model and thus locate more accurate salient regions. Moreover, a heuristic object-based dictionary from background superpixels is obtained in border set removing the image regions within the potential object regions. Experimental results on four large benchmark databases demonstrate that the proposed method performs favorably against eight recent state-of-the-art methods in terms of three evaluation criterions, with a reduction of MAE by 19.8% than GR and 29.3% than CB in the two SED datasets, respectively. Meanwhile, our method also runs faster than the comparative detection approaches. [ABSTRACT FROM AUTHOR]

Abstract: A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed in this paper. Firstly, an improved particle swarm optimization (PSO) is used to partition the training samples, the number of clusters must be given in advance, an automatic clustering algorithm rather than the trial and error is adopted to find the proper number of clusters, and a set of clustering centers is obtained to form classification mechanism. Secondly, in order to exploit more useful local information and get a better optimizing result, a global factor is introduced to the update strategy update strategy of particle in PSO. PSOSLCC has been extensively compared with fuzzy relational classifier (FRC), vector quantization and learning vector quantization (VQ+LVQ3), and radial basis function neural network (RBFNN), a simultaneous learning framework for clustering and classification (SCC) over several real-life datasets, the experimental results indicate that the proposed algorithm not only greatly reduces the time complexity, but also obtains better classification accuracy for most datasets used in this paper. Moreover, PSOSLCC is applied to a real world application, namely texture image segmentation with a good performance obtained, which shows that the proposed algorithm has a potential of classifying the problems with large scale. [Copyright &y& Elsevier]

Abstract: The goal of segmentation is to partition an image into disjoint regions. In this paper, the segmentation problem based on partition clustering is viewed as a combinatorial optimization problem. A new algorithm called a quantum evolutionary clustering algorithm based on watershed (QWC) is proposed. In the new algorithm, the original image is first partitioned into small pieces by watershed algorithm, and the quantum-inspired evolutionary algorithm is used to search the optimal clustering center, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with QICW, the genetic clustering algorithm based on watershed (W-GAC) and K-means algorithm based on watershed (W-KM). [Copyright &y& Elsevier]

Sparse Representation based Graphs (SRGs) have attracted increasing interests in very recent years. However, for lacking global constraints on solutions to sparse representation, SRGs cannot accurately reveal data structure when data are grossly corrupted. In this paper, in order to achieve robust classification of wide range of datasets when only a small number of labeled samples are available, we advance a new semi-supervised kernel low-rank representation graph (SKLRG), by combining low-rank representation (LRR) with graphs and kernel trick. A kernel projection is first learned to find high-dimensional space where data have possible low-rank structure. Then a low-rank representation of the projected data is calculated from which we can derive a SKLRG matrix to evaluate data affinity and classify corrupted patterns. The proposed SKLRG can naturally reveal the relationship among data in the projected space, and can capture the global structure of complex data and implements more robust subspace segmentation. Moreover, connected weights of SKLRG are refined by pairwise constrains where label information is explored to further improve the classification results. Some experiments are taken on some benchmark datasets and Synthetic Aperture Radar (SAR) images that are corrupted by speckle noise. The results show that the proposed SKLRG can achieve better performance than its counterparts when there are only a small number of labeled samples. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a new approach to interactive image segmentation via kernel propagation (KP), called KP Cut. The key to success in interactive image segmentation is to preserve characteristics of the user׳s interactive input and maintain data-coherence effectively. To achieve this, we employ KP which is very effective in propagating the given supervised information into the entire data set. KP first learns a small-size seed-kernel matrix, and then propagates it into a large-size full-kernel matrix. It is based on a learned kernel, and thus can fit the given data better than a predefined kernel. Based on KP, we first generate a small-size seed-kernel matrix from the user׳s interactive input. Then, the seed-kernel matrix is propagated into the full-kernel matrix of the entire image. During the propagation, foreground objects are effectively segmented from background. Experimental results demonstrate that KP Cut effectively extracts foreground objects from background, and outperforms the state-of-the-art methods for interactive image segmentation. [Copyright &y& Elsevier]

Abstract: Sparse coding based classifier (SCC) proves to lead to the state-of-the-art result in pattern recognition. Compared with traditional generative models and discriminative models, it neither casts some assumption on the distribution of data, nor learns a hyperplane to separate samples. However, SCC is characteristic of slow prediction because an -norm minimization need to be solved to assign the label for each sample. In this paper, we propose a Superpixel-wise Structural Sparse Coding based Classifier (S3CC) for image segmentation. An unsupervised superpixel segmentation is first used to derive the initial labeled samples, and SCC is extended to the semi-supervised pattern where unlabeled samples are incrementally labeled and taken as the dictionary to improve the classification accuracy. Moreover, a neighborhood spatial constraint is cast on the prediction of pixel labels, to avoid the speckle-like mis-segmentation of images. Some experiments are taken on some artificial texture images, to investigate the segmentation result of our proposed S3CC. Some aspects including (1) Comparison of S3CC with SCC, (2) Comparisons of S3CC with and without spatial constraint, (3) Comparison of S3CC with semi-supervised S3CC, are tested, and the results prove the efficiency and superiority of S3CC to its counterparts. [Copyright &y& Elsevier]

Abstract: The quantum clustering (QC) algorithm suffers from the issues of getting stuck in local extremes and computational bottleneck when handling large-size image segmentation. By embedding a potential evolution formula into affinity function calculation of multi-elitist immune clonal optimization, and updating the cluster center based on the distance matrix, the multi-elitist immune clonal quantum clustering algorithm (ME-ICQC) is proposed in this paper. In the proposed framework, elitist population is composed of the individuals with high affinity, which is considered to play dominant roles in the evolutionary process. It can help to find the global optimal solution or near-optimal solution for most tested tasks. The diversity of population can be well maintained by general subgroup evolution of ME-ICQC. These different functions are implemented by the dissimilar mutation strategies or crossover operators. The bi-group exchanges the information of excellence antibodies using the hypercube co-evolution operation. Compared with existing algorithms, the ME-ICQC achieves an improved clustering accuracy with more stable convergence, but it is not significantly better than other optimization techniques combined with QC. Also, the experimental results also show that our algorithm performs well on multi-class, parameters-sensitive and large-size datasets. [Copyright &y& Elsevier]

• This paper proposes a novel algorithm for segmentation of SAR image. • THFCM firstly divides the image into pixel groups to extract local feature. • The major pixels of each pixel group are selected to construct a thumbnail. • The thumbnail approach leverages local image information, helping to overcome speckle noise. • Experiments suggest that THFCM outperforms several other state-of-the-art algorithms in terms of both segmentation accuracy and running time. This paper proposes a novel algorithm for segmentation of synthetic aperture radar (SAR) image, our proposed algorithm (THFCM) is based on thumbnail representations and a hierarchical fuzzy C-means (FCM) approach. THFCM firstly divides the image into pixel groups to extract local feature, and the major pixels of each pixel group are selected to construct a thumbnail. FCM is then used to segment each thumbnail, and hierarchical segmentation is then performed on the overall image data, based on the results of thumbnail clustering. The thumbnail approach leverages local image information, helping to overcome speckle noise, while the hierarchical approach improves computational efficiency. Experiments on simulated and real SAR images suggest that THFCM outperforms several other state-of-the-art algorithms in terms of both segmentation accuracy and running time. [ABSTRACT FROM AUTHOR]