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Efficient image representation for object recognition via pivots selection

Frontiers of Computer Science 2015年第3期9卷 383-391页

作者： Bojun XIE Yi LIU HuiZHANG Jian YU Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well- known pre-defined patch-level descriptors such as scalein- variant feature transform （SIFT） and histogram of oriented gradient （HOG）, the kernel descriptor （KD） method [1] of- fers a new way to ＂grow-up＂ features from a match-kernel defined over image patch pairs using kernel principal compo- nent analysis （KPCA） and yields impressive results. In this paper, we present efficient kernel descriptor （EKD） and efficient hierarchical kernel descriptor （EHKD）, which are built upon incomplete Cholesky decomposition. EKD au- tomatically selects a small number of pivot features for gener- ating patch-level features to achieve better computational effi- ciency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD.

关键词： efficient kernel descriptor efficient hierarchi-cal kernel descriptor incomplete Cholesky decomposition,patch-level features image-level features

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Incremental approach for updating approximations of variable precision rough set based on dominance relations 3

Incremental approach for updating approximations of variable...

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3rd International Conference on Information Technology and Management Innovation, ICITMI 2014

作者： Li, Yan Hou, Jia Jia Liu, Xiao Qing Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783038352396

Variable precision rough set (VPRS) based on dominance relation is an extension of traditional rough set by which can handle preference-ordered information flexibly. This paper focuses on the maintenance of approximations in dominance based VPRS when the objects in an information system vary over time. The incremental updating principles are given as inserting or deleting an object, and some experimental evaluations validates the effectiveness of the proposed method. © 2014 Trans Tech Publications, Switzerland.

关键词： Rough set theory

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Maintaining dynamic information systems using incremental dominance-based rough set approach 3

Maintaining dynamic information systems using incremental do...

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3rd International Conference on Information Technology and Management Innovation, ICITMI 2014

作者： Li, Yan Liu, Xiao Qing Hou, Jia Jia Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783038352396

Dominance-based rough sets approach (DRSA) is an effective tool to deal with information with preference-ordered attribute domain. In practice, many information systems may evolve when attribute values are changed. Updating set approximations for these dynamic information systems is a necessary step for further knowledge reduction and decision making in DRSA. The purpose of this paper is to present an incremental approach when the information system alters dynamically with the change of condition attribute values. The updating rules are given with proofs, and the experimental evaluations on UCI data show that the incremental approach outperforms the original non-incremental one. © 2014 Trans Tech Publications, Switzerland.

关键词： Information systems

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learning the parameters for least squares support vector machine

Learning the parameters for least squares support vector mac...

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2011 7th International Conference on Natural Computation, ICNC 2011

作者： Lu, Shuxia Fan, Xiaoxue Hu, Lisha Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9781424499533

The regularization parameter and kernel parameter play important roles in the performance of the least squares support vector machine (LS-SVM). Aimed at optimizing the LS-SVM's parameters, a fast method based on distance is presented. The method is by way of calculating the various types of distances in the feature space to determine the optimal kernel parameter. Since the method only needs to calculate some simple mathematical formulas, and avoids training the corresponding LS-SVM classifiers, the method can greatly reduce the training time. Experiment results show that the proposed method can improve the training speed. © 2011 IEEE.

关键词： Support vector machines

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Image classification by combining local and mid-level features 18

Image classification by combining local and mid-level featur...

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2nd International Conference on Innovation in Artificial intelligence, ICIAI 2018

作者： Lu, Yao Zhang, Hui Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Hebei China

ISBN: (纸本)9781450363457

It is meaningful to study high performance image classification algorithms for massive image management and effective organization. Image feature representations directly affect the performance of classification algorithm, however, in the current popular sparse coding framework for image classification, the relationship between local features is neglected, and there is still considerable room left for further improvement. In this paper, mid-level features are extracted efficiently in the superpixel region, which embeds the structural information of local features. Then we combine the local features and mid-level features to achieve better discriminative ability under the sparse coding framework. Compared to using only local features, the new methods improve the classification performance about 3% on several popular benchmarks. © 2018 Association for Computing machinery.

关键词： Image classification

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A method for person name disambiguation based on Baidu Encyclopedia

A method for person name disambiguation based on Baidu Encyc...

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2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Li, Xinfu Cao, Wenxue Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9781457717017

The phenomenon of person name ambiguity is widespread on web pages in that one name may be used by different people. It is important to uniquely identify the given person on the web. In this paper, the method Baidu-PND is proposed by the authors. It is an unsupervised name disambiguation method based on Baidu Encyclopedia. We extract three features including background knowledge, contextual feature and Related-Set of the characters from the online Baidu Encyclopedia. The weights of the features are studied by logistic regression algorithm. Then we make a linear fusion of the features. The maximum combined value is selected as the correct person on web pages. Experiments are conducted to measure the performance of Baidu-PND, which show that the performance is higher than we expected, validating its feasibility and effectiveness for person name disambiguation on web pages. And, Baidu-PND is a new method for knowledge mining based on Baidu Encyclopedia. © 2011 IEEE.

关键词： Websites

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Selection of deep web database based on retrieval performance

Selection of deep web database based on retrieval performanc...

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2nd International Workshop on Education Technology and Computer Science, ETCS 2010

作者： Li, Weijing Yuan, Fang Zhang, Ming Key Lab. in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9780769539874

A mass of high-quality information included in Deep Web can be accessed, which is still growing rapidly with the rapid development of the World Wide Web. Therefore it becomes more and more important to find the Web databases which are most relevant to the user queries. In this paper we propose a selection method of Web database based on retrieval performance. This method can fix the topic based on website characteristics and then classify the websites. Finally it decides which Web database can be chosen based on the retrieval performance. This method can not only accurately select the Web databases which satisfy the user queries but also improve the speed of the database query and the quality of retrieval. © 2010 IEEE.

关键词： Websites

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Instances selection for NN with fuzzy rough technique

Instances selection for NN with fuzzy rough technique

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Kang, Xiao-Meng Liu, Xiao-Peng Zhai, Jun-Hai Zhai, Meng-Yao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The NN algorithm is a simple and well-known supervised learning scheme which classifies an unseen instance by finding its closest neighbor in training set. The main drawback of NN is that the whole training set must be stored in the computer to classify an unseen instance. In order to deal with this problem, P. Hart proposed the condensed nearest neighbor (CNN) algorithm. However, CNN select the important instances from the whole training set, which suffers from the problem of large memory requirement same as NN. In this paper, we propose an algorithm to select instances from the border region with fuzzy rough technique. The experimental results demonstrate the effectiveness of our proposed method. © 2011 IEEE.

关键词： Classification (of information)

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The condensed fuzzy k-nearest neighbor rule based on sample fuzzy entropy

The condensed fuzzy k-nearest neighbor rule based on sample ...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Zhai, Jun-Hai Li, Na Zhai, Meng-Yao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The fuzzy k-nearest neighbor (F-KNN) algorithm was originally developed by Keller in 1985, which generalized the k-nearest neighbor (KNN) algorithm and could overcome the drawback of KNN in which all of instances were considered equally important. However, the F-KNN algorithm still suffers from the problem of large memory requirement same as the KNN. In order to deal with the problem, this paper proposes the condensed fuzzy k-nearest neighbor rule (CFKNN) which selects the important instances based on sample fuzzy entropy. The experimental results show that our proposed method is feasible and effective. © 2011 IEEE.

关键词： Motion compensation

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Potential support vector machine based on the reduced samples

Potential support vector machine based on the reduced sample...

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International Conference on Information Science and Engineering

作者： Lu, Shu-Xia Cao, Gui-En Meng, Jie Wang, Hua-Chao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9781424480968

When the training dataset is very large, the learning process of potential support vector machine takes up so large memory that the training speed is very slow. To accelerate the training speed of the potential support vector machine (PSVM) for large-scale datasets, a new method is proposed, which introduces PSVM based on the reduced samples. The new method removes most non-support vectors, and keeps the samples on and near the boundary, which may be the support vectors, as the new training samples. This method is more suitable to large-scale datasets. The experimental results show that the proposed method performs well to decrease the consumption of computer memory,and accelerate the training speed of PSVM. © 2010 IEEE.

关键词： Support vector machines

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