Effective document classification is a long-pursued goal in knowledge management. This paper proposes a novel hybrid approach of semantic representation and statistical measurements. Document is divided into content s...
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A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the ...
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We propose two geometric structure based approaches GGCI (global geometric clustering for image) and GSIM (geometric structure based image matching) for image clustering and image matching, respectively. For face imag...
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ISBN:
(纸本)9781424404759
We propose two geometric structure based approaches GGCI (global geometric clustering for image) and GSIM (geometric structure based image matching) for image clustering and image matching, respectively. For face images or object images taken with varying factors, the GGCI approach learns the global geometric structure of images space and clusters images based on geodesic distance instead of Euclidean distance and the extended nearest neighbor approach. The GSIM approach uses the minimal Euclidean distance between parts of image and the pattern and its variations as matching criteria and threshold strategy for image matching. We demonstrate experimentally that the GGCI approach achieves lower error rates and the GSIM approach brings down the sensitivity of gray values to change in radiometry and reduces multi local extrema to some extent.
In this paper, Adaboost and SVM are applied to SAR ATR (synthetic aperture radar automatic target recognition) respectively. The performance of these two classifiers is analyzed and compared in target aspect window wi...
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In this paper, Adaboost and SVM are applied to SAR ATR (synthetic aperture radar automatic target recognition) respectively. The performance of these two classifiers is analyzed and compared in target aspect window with different size. First, PCA (principal component analysis) features are selected as target feature, and then *** and SVM are used to classify, respectively. Experimental results based on MSTAR data sets show that Adaboost classifier has better robustness than SVM classifier
In this paper, a unique road contour extraction approach from high resolution satellite image is proposed, in which the road contour was extracted in two steps. Firstly, support vector machines (SVM) was employed mere...
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In this paper, a unique road contour extraction approach from high resolution satellite image is proposed, in which the road contour was extracted in two steps. Firstly, support vector machines (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. The identified road group images are the discrete and irregularly distributed sampled points, and they are an uncompleted data set for the road. Secondly, the road contour was extracted from the road group images using the tensor voting framework, since the tensor voting technique is superior to the traditional methods in extracting the geometrical structure from the uncompleted data set. The experimental results on the high resolution satellite image demonstrate that the proposed approach worked well with images comprised by both rural and urban area features.
Semi-G2 basis functions are introduced, the degree of which is larger than three. These basis functions are expressed explicitly via matrices decomposition. Based on them, equations for constructing G2 splines can be ...
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Semi-G2 basis functions are introduced, the degree of which is larger than three. These basis functions are expressed explicitly via matrices decomposition. Based on them, equations for constructing G2 splines can be presented independently of geometric shape parameters' values. It makes the equation's solving easier. Analysis shows that this method may be extended to be applicable for constructing Gn splines.
The Resource Space Model (RSM) is a semantic data model based on orthogonal classification semantics for effectively managing various resources in interconnection environment. In parallel with the integrity theories o...
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Linear classifiers are of great significance in the classification field. In this paper, with meticulous studies on the linear classifiers, we gained a general framework for constructing fast trained linear classifier...
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To improve search efficiency and reduce unnecessary traffic in Peer-to-Peer (P2P) networks, this paper proposes a trust-based probabilistic search algorithm, called preferential walk (P-Walk). Every peer ranks its nei...
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ISBN:
(纸本)1595930515
To improve search efficiency and reduce unnecessary traffic in Peer-to-Peer (P2P) networks, this paper proposes a trust-based probabilistic search algorithm, called preferential walk (P-Walk). Every peer ranks its neighbors according to searching experience. The highly ranked neighbors have higher probabilities to be queried. Simulation results show that P-Walk is not only efficient, but also robust against malicious behaviors. Furthermore, we measure peers' rank distribution and draw implications.
The key issue of Peer Data Management Systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers...
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