The Experts below are selected from a list of 15039 Experts worldwide ranked by ideXlab platform
Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.
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adaptive multiobjective memetic Fuzzy Clustering Algorithm for remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:Due to the intrinsic complexity of remote sensing images and the lack of prior knowledge, Clustering for remote sensing images has always been one of the most challenging tasks in remote sensing image processing. Recently, Clustering methods for remote sensing images have often been transformed into multiobjective optimization problems, making them more suitable for complex remote sensing image Clustering. However, the performance of the multiobjective Clustering methods is often influenced by their optimization capability. To resolve this problem, this paper proposes an adaptive multiobjective memetic Fuzzy Clustering Algorithm (AFCMOMA) for remote sensing imagery. In AFCMOMA, a multiobjective memetic Clustering framework is devised to optimize the two objective functions, i.e., $Jm$ and the Xie-Beni $(XB) $ index. One challenging task for memetic Algorithms is how to balance the local and global search capabilities. In AFCMOMA, an adaptive strategy is used, which can adaptively achieve a balance between them, based on the statistical characteristic of the objective function values. In addition, in the multiobjective memetic framework, in order to acquire more individuals with high quality, a new population update strategy is devised, in which the updated population is composed of individuals generated in both the local and global searches. Finally, to evaluate the proposed AFCMOMA Algorithm, experiments using three remote sensing images were conducted, which confirmed the effectiveness of the proposed Algorithm.
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An Adaptive Memetic Fuzzy Clustering Algorithm With Spatial Information for Remote Sensing Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:Due to its inherent complexity, remote sensing image Clustering is a challenging task. Recently, some spatial-based Clustering approaches have been proposed; however, one crucial factor with regard to their Clustering quality is that there is usually one parameter that controls their spatial information weight, which is difficult to determine. Meanwhile, the traditional optimization methods of the objective functions for these Clustering approaches often cannot function well because they cannot simultaneously possess both a local search capability and a global search capability. Furthermore, these methods only use a single optimization method rather than hybridizing and combining the existing Algorithmic structures. In this paper, an adaptive Fuzzy Clustering Algorithm with spatial information for remote sensing imagery (AFCM_S1) is proposed, which defines a new objective function with an adaptive spatial information weight by using the concept of entropy. In order to further enhance the capability of the optimization, an adaptive memetic Fuzzy Clustering Algorithm with spatial information for remote sensing imagery (AMASFC) is also proposed. In AMASFC, the Clustering problem is transformed into an optimization problem. A memetic Algorithm is then utilized to optimize the proposed objective function, combining the global search ability of a differential evolution Algorithm with a local search method using Gaussian local search (GLS). The optimal value of the specific parameter in GLS, which determines the local search efficiency, can be obtained by comparing the objective function increment for different values of the parameter. The experimental results using three remote sensing images show that the two proposed Algorithms are effective when compared with the traditional Clustering Algorithms.
Yanfei Zhong - One of the best experts on this subject based on the ideXlab platform.
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adaptive multiobjective memetic Fuzzy Clustering Algorithm for remote sensing imagery
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:Due to the intrinsic complexity of remote sensing images and the lack of prior knowledge, Clustering for remote sensing images has always been one of the most challenging tasks in remote sensing image processing. Recently, Clustering methods for remote sensing images have often been transformed into multiobjective optimization problems, making them more suitable for complex remote sensing image Clustering. However, the performance of the multiobjective Clustering methods is often influenced by their optimization capability. To resolve this problem, this paper proposes an adaptive multiobjective memetic Fuzzy Clustering Algorithm (AFCMOMA) for remote sensing imagery. In AFCMOMA, a multiobjective memetic Clustering framework is devised to optimize the two objective functions, i.e., $Jm$ and the Xie-Beni $(XB) $ index. One challenging task for memetic Algorithms is how to balance the local and global search capabilities. In AFCMOMA, an adaptive strategy is used, which can adaptively achieve a balance between them, based on the statistical characteristic of the objective function values. In addition, in the multiobjective memetic framework, in order to acquire more individuals with high quality, a new population update strategy is devised, in which the updated population is composed of individuals generated in both the local and global searches. Finally, to evaluate the proposed AFCMOMA Algorithm, experiments using three remote sensing images were conducted, which confirmed the effectiveness of the proposed Algorithm.
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An Adaptive Memetic Fuzzy Clustering Algorithm With Spatial Information for Remote Sensing Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:Due to its inherent complexity, remote sensing image Clustering is a challenging task. Recently, some spatial-based Clustering approaches have been proposed; however, one crucial factor with regard to their Clustering quality is that there is usually one parameter that controls their spatial information weight, which is difficult to determine. Meanwhile, the traditional optimization methods of the objective functions for these Clustering approaches often cannot function well because they cannot simultaneously possess both a local search capability and a global search capability. Furthermore, these methods only use a single optimization method rather than hybridizing and combining the existing Algorithmic structures. In this paper, an adaptive Fuzzy Clustering Algorithm with spatial information for remote sensing imagery (AFCM_S1) is proposed, which defines a new objective function with an adaptive spatial information weight by using the concept of entropy. In order to further enhance the capability of the optimization, an adaptive memetic Fuzzy Clustering Algorithm with spatial information for remote sensing imagery (AMASFC) is also proposed. In AMASFC, the Clustering problem is transformed into an optimization problem. A memetic Algorithm is then utilized to optimize the proposed objective function, combining the global search ability of a differential evolution Algorithm with a local search method using Gaussian local search (GLS). The optimal value of the specific parameter in GLS, which determines the local search efficiency, can be obtained by comparing the objective function increment for different values of the parameter. The experimental results using three remote sensing images show that the two proposed Algorithms are effective when compared with the traditional Clustering Algorithms.
Xinbo Gao - One of the best experts on this subject based on the ideXlab platform.
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a novel Fuzzy Clustering Algorithm with non local adaptive spatial constraint for image segmentation
Signal Processing, 2011Co-Authors: Feng Zhao, Hanqiang Liu, Licheng Jiao, Xinbo GaoAbstract: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.
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a new feature weighted Fuzzy Clustering Algorithm
Granular Computing, 2005Co-Authors: Xinbo Gao, Licheng JiaoAbstract:In the field of cluster analysis, the Fuzzy k-means, k-modes and k-prototypes Algorithms were designed for numerical, categorical and mixed data sets respectively. However, all the above Algorithms assume that each feature of the samples plays an uniform contribution for cluster analysis. To consider the particular contributions of different features, a novel feature weighted Fuzzy Clustering Algorithm is proposed in this paper, in which the ReliefF Algorithm is used to assign the weights for every feature. By weighting the features of samples, the above three Clustering Algorithms can be unified, and better classification results can be also achieved. The experimental results with various real data sets illustrate the effectiveness of the proposed Algorithm.
Anil Kumar Gupta - One of the best experts on this subject based on the ideXlab platform.
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A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm
International Journal of Computer Trends and Technology, 2014Co-Authors: Dibya Jyoti Bora, Anil Kumar GuptaAbstract:Data Clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important Clustering technique based on Fuzzy logic. Also we have some hard Clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy Clustering Algorithm and hard Clustering Algorithm.
Feng Zhao - One of the best experts on this subject based on the ideXlab platform.
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a multiobjective spatial Fuzzy Clustering Algorithm for image segmentation
Soft Computing, 2015Co-Authors: Feng Zhao, Hanqiang Liu, Jiulun FanAbstract:Multiobjective spatial Fuzzy Clustering for image segmentation is proposed.The non-local spatial information of an image is introduced into fitness functions.The final solution is chosen by a cluster index with non-local spatial information.The proposed method can automatically evolve the number of clusters.Experiments on noisy images demonstrate the superiority of the proposed method. This article describes a multiobjective spatial Fuzzy Clustering Algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global Fuzzy compactness and Fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final Clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, Fuzzy c-means, two Fuzzy c-means Clustering Algorithms with spatial information and a multiobjective variable string length genetic Fuzzy Clustering Algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.
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a novel Fuzzy Clustering Algorithm with non local adaptive spatial constraint for image segmentation
Signal Processing, 2011Co-Authors: Feng Zhao, Hanqiang Liu, Licheng Jiao, Xinbo GaoAbstract: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.
