We propose an efficient method for automatically extracting express waybills from parcel images, which is challenging due to varied resolution of parcel images, arbitrary direction of waybills and different informatio...
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ISBN:
(纸本)9781467399623
We propose an efficient method for automatically extracting express waybills from parcel images, which is challenging due to varied resolution of parcel images, arbitrary direction of waybills and different information filled by senders. To address these challenges, logo matching is employed to extract the waybills. We begin by extracting scale-invariant feature-transformation (SIFT) keypoints from both the reference logo image and a parcel image, and matching them subject to a consistent projective transformation (homography) by using random sample consensus (RANSAC). Once the homography matrix is computed, we extract the waybill of parcel image by mapping all pixels from a standard waybill image to the parcel image. Experimental results on test datasets demonstrate the effectiveness of the proposed method.
We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online ...
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Hadoop MapReduce has been proven an effective computing model to deal with big data for the last few years. However, one technical challenge facing this framework is how to predict the execution time of an individual ...
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In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborho...
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ISBN:
(纸本)9781509003914
In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC; 2) NCC reduces the size of dictionary when compared with CRC.
Automatic image annotation is a significant and challenging problem in pattern recognition and computer vision. Existing models did not describe the visual representations of corresponding keywords, which would lead t...
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Subjective statement screening is a fundamental part of sentiment analysis of product reviews. Its aim is to extract the subjective texts from product reviews and filter objective texts that do not contain any sentime...
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Considering the fraudulent behavior of Quality of Service(QoS) for web services,a method to monitor the QoS deviation was proposed in this ***,we used random variable to describe the uncertainty of ***,the divergence ...
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ISBN:
(纸本)9781479970162
Considering the fraudulent behavior of Quality of Service(QoS) for web services,a method to monitor the QoS deviation was proposed in this ***,we used random variable to describe the uncertainty of ***,the divergence of QoS in different time periods was tested with Kolmogorov Smirnov(K-S) ***,we utilized stochastic dominance to judge the better one of *** analysis and experimental data showed that the proposed method was reasonable and effective.
In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis metho...
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ISBN:
(纸本)9781509006243
In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.
In the current social background,Existing facilities and tools already can not meet the needs of big data in expanding and analysis ***'s data storage and analysis work is achieved under cloud conditions and Hadoo...
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ISBN:
(纸本)9789462520677
In the current social background,Existing facilities and tools already can not meet the needs of big data in expanding and analysis ***'s data storage and analysis work is achieved under cloud conditions and Hadoop were set *** the conditions for cloud computing,cloud computing applications who have remote data files were not authorized to read its contents,which results in unauthorized manipulation,and it will produce a lot of security risks for large *** this paper,according to the cloud of different modes,Hadoop different stages of the operation,subject to the threat of non- confidence and security to steal big data generated to analyze a variety of privacy,with threat model as an example,it explores ways to address security threats.
This paper reports our submissions to community question answering task in SemEval-2015, which consists of two subtasks: (1) predict the quality of answers to given question as good, bad, or potentially relevant and (...
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