Relevance feedback schmes based on support vector machines(SVM) have been widely used in content-based image retrieval(CBIR). SVM-based relevance feedback has often bad performance when the number of labeled positive ...
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ISBN:
(纸本)9781424420209
Relevance feedback schmes based on support vector machines(SVM) have been widely used in content-based image retrieval(CBIR). SVM-based relevance feedback has often bad performance when the number of labeled positive feedback samples is small. This paper presents a method to use the unlabeled data to improve the performance of SVM classifier, which has only a few labeled training examples. We adapt an improved active learning approach to select most informative data from the unlabeled samples set. It can reduce to compute some unnecessary information for feedback results and only label few samples. It can be used in pervative computing availably. Experiments show our approach can use the unlabeled samples effectively, reduce to label more unnecessary data, and improve the classifier's performance.
We propose MarSelHR, a haplotype reconstruction system, which is a combination of LD based block partitioning with Clark's algorithm. MarSelHR uses less CPU processing time than other haplotype reconstruction syst...
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We propose MarSelHR, a haplotype reconstruction system, which is a combination of LD based block partitioning with Clark's algorithm. MarSelHR uses less CPU processing time than other haplotype reconstruction systems, and makes possible large-scale data processing using less memory through block unit processing. Furthermore, MarSelHR involves biological meaning through LD based partitioning and is able to result in more precise inference.
With the research and analysis on similarity measures which are commonly used in cross-media retrieval and content based image retrieval (CBIR), a new method called trigonometric function distance is proposed. This me...
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With the research and analysis on similarity measures which are commonly used in cross-media retrieval and content based image retrieval (CBIR), a new method called trigonometric function distance is proposed. This method satisfies metric properties, and is better than Euclidean distance and Minkowski distance in image similarity. To support this new theory, an algorithm for object shape analysis is designed, and experiments based on trigonometric function distance are conducted. Experiments give an encouraging high recognition rate by using the new similarity measurement
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