The multilevel thresholding problem is a challenge task due to the fact that the computation is usually very time-consuming for obtaining the optimal multilevel thresholds. Though the state-of-the-art multilevel thres...
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In order to improve the detection accuracy of spliced images, a new blind detection based on visual saliency was proposed in this paper. Firstly, create the edge conspicuous map by an improved OSF-based method, and ex...
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Hidden Naive Bayes (HNB) has demonstrated remarkable progress in classification accuracy, accurate class probability estimation and ranking. Since HNB is based on one-dependence estimators to get the approximate value...
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XML documents cluster analysis is a hot research topic. Researchers proposed a number of methods to cluster XML document collections. Boosting is successful well-known methods for improving the quality of clustering. ...
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We describe a novel mobile visual search system based on the saliency mechanism and sparse coding principle of the human visual system (HVS). In the feature extraction step, we first divide an image into different reg...
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We describe a novel mobile visual search system based on the saliency mechanism and sparse coding principle of the human visual system (HVS). In the feature extraction step, we first divide an image into different regions using the saliency extraction algorithm. Then scale-invariant feature transform (SIFT) descriptors in all regions are extracted while regional identities are preserved based on their various saliency levels. According to the sparse coding principle in the HVS, we adopt a local neighbor preserving Hash function to establish the binary sparse expression of the SIFT features. In the searching step,
In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens o...
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In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features （genes）, they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter （q0） in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to＂ generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.
In this paper, we propose a novel image retrieval method based on mutual information descriptors (MIDs). Under the physiological property of human eyes and human visual perception theory, MIDs are extracted to encode ...
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This paper finds the most expressive segments of a shape category called similar and discriminative parts, which can distinguish the learned shape class from other groups. The proposed model chooses a computationally ...
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engineering systems have become quite complicated in recent days. The requirements of design are complex and it is hard to meet them by considering only one discipline. In this paper, we suggest a hybrid Multi-objecti...
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In this paper, a Loop Tightness Algorithm (LTA) is proposed. First, it finds the network loops and calculates it tightness value quickly. Then, it obtains the communities of the networks based on the tightness values....
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In this paper, a Loop Tightness Algorithm (LTA) is proposed. First, it finds the network loops and calculates it tightness value quickly. Then, it obtains the communities of the networks based on the tightness values. Finally, it reveals the relationship between the network loops and the community structure. The LTA is tested and validated by means of synthetic networks and real networks.
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