Schema matching is the task of finding semantic correspondences between elements of two schemas, which plays a key role in many database applications, such as data integration, electronic commerce, data warehouse, sem...
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Schema matching is the task of finding semantic correspondences between elements of two schemas, which plays a key role in many database applications, such as data integration, electronic commerce, data warehouse, semantic query processing, and XML message exchange, etc. Especially, it is a basic research issue in metadata management. Unfortunately, it still remains largely a manual, labor-intensive, and expensive process. In this paper, the schema matching problem is treated as a combinatorial problem. Firstly, schemas are transformed into multi-labeled graphs, which are the internal schema model for schema matching. Therefore, the schema matching problem is reduced to the labeled graph matching problem. Secondly, a generic graph similarity measure is discussed, which uses the labels of nodes and the edges to compute the similarity between the two schemas. Then, an objective function based on the multi-labeled graph similarity is proposed. Based on the objective function, a greedy matching algorithm is designed to find the desired matching state for schema matching. A prominent characteristic of this method is that the algorithm combines the feasible matching information to obtain optimal matching. Finally, some schema samples are used to test the greedy matching algorithm. The test results confirm that the algorithm is effective, which can obtain mapping results with high quality.
This paper presents a novel approach for point target detection of sea-clutter SAR images. Traditional methods for this application can be classified into two aspects: threshold segmentation based on intensity differe...
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This paper presents a novel approach for point target detection of sea-clutter SAR images. Traditional methods for this application can be classified into two aspects: threshold segmentation based on intensity difference and target extraction based on suitable denoising. However, they appear to be not effective enough especially when sea clutter is strong. Taking advantage of the essentials of both methods, a effective approach using space separation is developed based on fractal theory and independent component analysis. First, pointwise Holder exponent are computed and binary-fuzzy processing is used for enhancement;then, basis images and independent components of the processed image are respectively obtained by ICA technique. After that, according to separation criterion, the original space is separated into two subspaces called clean-space and noise-space with respective independent components and corresponding basis images. Finally, the recovery image is obtained after enhancing the independent components in clean-space. As the results show, the proposed method is validated and point target is extracted more efficiently compared with conventional ones.
ServiceBSP model is presented as an extension of BSP model with a view to the advantages of BSP model in Grid environment where large-scale and geographically distributed resources (abstracted as services) are availab...
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In this paper, we propose a scheme for moving object tracking from videos by combining mean shift and motion field statistics. For mean shift, we employ an enhanced spatial-range mean shift that enables a reduced numb...
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In this paper, we present a novel V-system descriptor for 2D models;the descriptor is invariant against the rotation, translation, scale transform, and can remove the effect of the arbitrary start point of the shape t...
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In this paper, we present a novel V-system descriptor for 2D models;the descriptor is invariant against the rotation, translation, scale transform, and can remove the effect of the arbitrary start point of the shape through the principal orientation. We show that V-system descriptor can be computed efficiently by the fast V-system transformation, and be good at using multi resolution analysis through weighted Euclidean distance. So the new V-system descriptor has some resistance to slightly occlusion, and may achieve good recognition property according to the actual situation.
In this paper, a novel neural network based manifold learning method(NNBML)[1] recently appeared in the Journal of Science is introduced. It can effectively convert high-dimensional data into low-dimensional codes, wh...
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ISBN:
(纸本)9781601320438
In this paper, a novel neural network based manifold learning method(NNBML)[1] recently appeared in the Journal of Science is introduced. It can effectively convert high-dimensional data into low-dimensional codes, which are then used for classification. However, it performs not well while dealing with small size face database used for face recognition. We propose a solution generating more samples data based on the existing data. The proposed method is implemented on two well-known face databases, viz. ORL and Yale face databases. The experimental results show that NNBML is able to deal with the task of face recognition after more data samples generated using the proposed method, and also that NNBML outperforms LDA in terms of recognition rate.
In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Pri...
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Automated tongue image segmentation in tongue diagnosis system of traditional Chinese medicine is difficult due to two factors: There are lots of pathological details on the surface of tongue, and the shapes of tongue...
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In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face recognition in varying lighting. Based on the observation that the ordinal relationship between...
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ISBN:
(纸本)9780819469526
In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face recognition in varying lighting. Based on the observation that the ordinal relationship between the average brightness of image regions pair is invariant under lighting changes, Local Binary Mapping is defined as an illumination invariant for face recognition based on Local Binary pattern descriptor, which extracts the local variance features of an image. For the 'symbol' feature vector, hamming distance is used as similarity measurement. It has been proved that the proposed method can provide the accuracy of 100 percent for subset 2, 3, 4 and 98.89 percent for subset 5 of the Yale facial database B when all images in subset 1 are used as gallery.
The sliding window method will cause the severe unbalanced dataset problem. In this paper, under-sample the majority class method is adopted to solve this problem,and SVM is used to classify the processed data The bet...
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The sliding window method will cause the severe unbalanced dataset problem. In this paper, under-sample the majority class method is adopted to solve this problem,and SVM is used to classify the processed data The better prediction result of minority class (that is, the signal peptides positive sample set) is ***, we discover that the (-3,-1) rule is helpful to the prediction. So Information content based feature weighting method is proposed This method avoids the blindness of the previous algorithm in dealing with different sites. Experiments show that not only is the correct prediction rate of minority class improved dramatically, but also the correct prediction rate of majority class is kept in a high *** of the unbalanced data processing and the proposed information content based feature weighting method can greatly improve the performance of SVM classifier of signal peptides.
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