The excellent feature set or feature combination of cotton foreign fibers is great significant to improve the performance of machine-vision-based recognition system of cotton foreign fibers. To find the excellent feat...
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ISBN:
(纸本)9783319483566
The excellent feature set or feature combination of cotton foreign fibers is great significant to improve the performance of machine-vision-based recognition system of cotton foreign fibers. To find the excellent feature sets offoreign fibers, in this paper presents three metaheuristic-based feature selection approaches for cotton foreign fibers recognition, which are particle swarm optimization, ant colony optimization and genetic algorithm, respectively. The k-nearest neighbor classifier and support vector machine classifier with k-fold cross validation are used to evaluate the quality offeature subset and identify the cotton foreign fibers. The results show that the metaheuristic-based feature selection methods can efficiently find the optimal feature sets consisting of a few features. It is highly significant to improve the performance of recognition system for cotton foreign fibers.
The paper aims at tackling the problem of image fusion for panchromatic CT and multispectral CBF images. We proposed a fusion algorithm based on Intensity-hue-saturation (IHS) transform and Discrete Wavelet Transform ...
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The paper aims at tackling the problem of image fusion for panchromatic CT and multispectral CBF images. We proposed a fusion algorithm based on Intensity-hue-saturation (IHS) transform and Discrete Wavelet Transform (DWT) in the paper. We use different fusion rules for different parts of the images. The area energy is adopted to fuse the high frequency parts of the original images. While, for the low frequency parts, weighted averaging is applied. Experimental results show that the proposed algorithm is not only competent for retaining the spatial resolution of the panchromatic image, but also solves the problem of spectral distortions.
XML document is dynamic and dynamic XML document collection cluster analysis is a hot research topic. This paper proposed a data model named TDOM to record the dynamic changing process of XML document, then proposed a...
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XML document is dynamic and dynamic XML document collection cluster analysis is a hot research topic. This paper proposed a data model named TDOM to record the dynamic changing process of XML document, then proposed a method to discover all the conspicuous frequent substructures from TDOM dataset, finally, proposed a method to cluster the XML documents by the conspicuous frequent substructures. The experiment runs on the synthetic dataset, the experimental result shows that our method is efficiency and scalable.
We propose a new multi-focus image fusion algorithm which is based on Difference Transform (DT). In this paper, we investigate the use of Difference Transform in image focus detection in Laplacian Pyramid and Discrete...
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Mining newsworthy events from a large number of microblogging information is not only the primary problem that several big microblogging websites need to solve, but also a new research field in micro-information age. ...
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Image fusion is a procedure in which two or more images of one scene captured by different sensors are combined into one image. The target of image fusion is to produce images which are more suitable for human visual ...
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In this letter, we suggest a novel Object Shrunken (OS) algorithm to handle the image classification task. Unlike the prior art, this letter considers the foreground or the object location in the image for more discri...
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In this letter, we suggest a novel Object Shrunken (OS) algorithm to handle the image classification task. Unlike the prior art, this letter considers the foreground or the object location in the image for more discriminative image-level representation. The OS algorithm suggests a straightforward procedure to box the object location. It first proposes a Weighted Local Outlier Factor (WLOF) to remove all the interest point outliers, and then positions the object location in terms of the distribution of the rest interest points. We evaluate the proposed algorithm on the well-known dataset Caltech-101. The resulting OS algorithm outperforms the state-of-art approaches in the image classification task.
Point pattern matching is the basis of image recognition and computer vision. Point pattern matching in three dimensional space with the presence of noise and outlier is an important research focus. In this paper, we ...
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With the scale of social networks growing rapidly, the amount of user participating in it increases at astonishing speed. Predicting user influence in social networks is an interesting and useful research direction. T...
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Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made...
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Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery - a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.
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