Feature selection (FS) is a most important step which can affect the performance of patternrecognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO ...
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Feature selection (FS) is a most important step which can affect the performance of patternrecognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm
In this paper, we present a new segmentation model, which makes uses of Curvelet's advantages of edge preserving and noise averaging. The model first applies Lorentzian-function based diffusion for stable pixel cl...
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In this paper, we present a new segmentation model, which makes uses of Curvelet's advantages of edge preserving and noise averaging. The model first applies Lorentzian-function based diffusion for stable pixel clustering, and then projects boundaries by Curvelet transform (CT) to enhance edges and modify region smear in diffusion. In particular, we also propose a criterion to seek the appropriate moment for CT enhancement, it is fulfilled by comparing partition results of Lorentzian and Tukey-based functions. If the number of reduced regions between two adjacent segmentation rounds arrives a threshold, CT will be performed to prevent edge disappearing. Experiments show that this significant segmentation is resulted from CT's properties of boundary keeping and denoising, the method is superior to many other PDE approaches.
As a natural consequence of steady increase of average population age in developed countries, Alzheimer's disease is becoming an increasingly important public health concern. The financial and emotional toll of th...
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As a natural consequence of steady increase of average population age in developed countries, Alzheimer's disease is becoming an increasingly important public health concern. The financial and emotional toll of the disease is exacerbated with lack of standard diagnostic procedures available at the community clinics and hospitals, where most patients are evaluated. In our recent preliminary results, we have reported that the event related potentials (ERPs) of the electroencephalogram can be used to train an ensemble-based classifier for automated diagnosis of Alzheimer's disease. In this study, we present an updated alternative approach by combining complementary information provided by ERPs obtained from several parietal region electrodes. The results indicate that ERPs obtained from parietal region of the cortex carry substantial complementary diagnostic information. Specifically, the diagnostic ability of such an approach is substantially better, compared to the performance obtained by using data from any of the individual electrodes alone. Furthermore, the diagnostic performance of the proposed approach compares very favorably to that obtained at community clinics and hospitals.
The great heterogeneity of Web based learning systems storing and providing digital e-learning data requires the introduction of interoperability aspects in order to resolve integration problems in a flexible and dyna...
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The great heterogeneity of Web based learning systems storing and providing digital e-learning data requires the introduction of interoperability aspects in order to resolve integration problems in a flexible and dynamic way. Learning objects developed and stored in many different places on the Web have a tremendous potential to benefit e-learning in particular and education in general. So the distributed e-learning data integration is significant for the enforcement of novel searching mechanisms in distributed e-learning repositories. Our approach introduces an advanced search mechanism which initializes a semantic model representing the digital content stored in distributed e-learning servers, through the use of ontologies. In order to retrieve useful content to be added to the learning environment while bypassing compatibility problems between different ontological representations of the same domain, we use a mixed approach constituting both keywords and ontologies. Searching tasks are carried out in the metadata level, where information concerning Web digital content is published, managed and stored in the form of a scalable description of knowledge domains
Feature Filtering is an approach that is widely used for dimensionality reduction in text categorization. In this approach feature scoring methods are used to evaluate features leading to selection. Thresholding is th...
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In this paper, we propose a novel classification algorithm, called geometrical probability covering (GPC) algorithm, to improve classification ability. On the basis of geometrical properties of data, the proposed algo...
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Face verification is useful in a variety of applications. A face verification system is vulnerable not only to variations in ambient lighting, facial expression and facial pose, but also to the effect of small sample ...
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Background subtraction is widely used as an effective method for detecting moving objects in a video image. However, background subtraction requires a prerequisite in that image variation cannot be observed, and the r...
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This paper will discuss a method for presenting a surveillance image captured by an active camera such as one that actively tracks a moving subject as an image that is easily understood by an observer. Active camera s...
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In this contribution, the application of evolutionary Lainiotis' algorithms in real-world adaptive system identification problems is presented. These algorithms combine the effectiveness of adaptive multi model pa...
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