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Focusing on the problem of resource allocation under large-scale, distributed, autonomous, heterogeneous and dynamic environments in grid computing, a heuristic algorithm combining fuzzy clustering with application preference is proposed. Fuzzy clustering method is applied according to a group of features, which describe the user's application preference, to realize reasonable pre-classification resource...
Organizing images into meaningful (semantically) categories using low-level visual features is a challenging and important problem in content-based image retrieval. Clustering algorithms make it possible to represent visual features of images with finite symbols. However, there are two problems in most current image clustering algorithms. One is without considering the choice of the initial cluster...
Immunohistochemical color image segmentation has important application value for quantitative assessment of immunohistochemical image. In this paper, an automatic segmentation method was proposed according to characteristics of color immunohistochemical images. First of all, we established a Chromatics criteria in RGB space so that positive cells regional and negative cells region were separated automatically...
In order to improve the training efficiency to the data set, an improved adaptive Support Vector Machine (SVM) algorithm with combinational Fuzzy C-means Clustering is proposed. With multi-layer fuzzy C-means clustering algorithm original data are pretreated to remove the training data, which has no contribution to the classification. The remaining data are used to complete the training work for SVM...
In this paper, we propose a fast semi-supervised learning algorithm based on the bisecting clustering. The key idea of the proposed algorithm is dividing data into two sub clusters each time by using bisecting clustering and parts of the features of the data. The time complexity of the algorithm is nearly linear to the data size. Numerical comparisons with several existing methods for the UCI datasets...
Traditional Fuzzy c-means (FCM) algorithm is commonly used in unsupervised learning. However, there are some limitations. Cluster number should be determined and the cluster center should be initialized before classification. A new algorithm is proposed in the paper. The best cluster number is obtained by analyzing cluster validity function and the cluster center is initialized by HCM. The data set...
Nowadays, main methods used to SAR imagery built-up areas classification are GLCM (gray-level co-occurrence matrix) textural analysis, Markov random field, etc. They are extraordinarily time consumption and need for manual interaction. In this paper, a new scheme for fast and automatic classification of built-up areas is presented. It is based on geostatistical texture analysis and mainly consists...
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