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	1. Nonparametric Kernel-Based Distribution Modeling of Bioelectrical Impedance Features for Breast Tissue Classification
	LU Meng,WU Yunfeng
	Computer Science and Technology 28 April 2013
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	Abstract:Classification of breast tissues helps assess early stage pathological conditions in the cancerating breast. In this paper, we present a nonparametric modeling method to estimate the bivariate probability densities of features for the normal and pathological breast tissues. Two representative bioelectrical features were first selected for classification by using the Kruskal-Wallis test and correlation analysis. The bivariate feature density was estimated using Gaussian kernels, and the nonlinear classification was performed using the maximal posterior probability method. The results showed that the kernel-based maximal posterior probability (KMPP) classification provided an accurate rate of 84.91% and the area under receiver operating characteristic (ROC) curve of 0.9307. The diagnostic performance and the nonlinear decision boundary of the proposed KMPP method were better than Fisher's linear discriminant analysis (accuracy: 83.02%, area under ROC curve: 0.8789).
	TO cite this article:LU Meng,WU Yunfeng. Nonparametric Kernel-Based Distribution Modeling of Bioelectrical Impedance Features for Breast Tissue Classification[OL].[28 April 2013] http://en.paper.edu.cn/en_releasepaper/content/4540663


	2. CLOF: A Noise Removal Algorithm Based on Combined Local Outlier Factors
	REN Yi-li,WU Jun-jie,XIONG Hai-tao,LIU Chen
	Computer Science and Technology 22 January 2013
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	Abstract:Real-world data is never perfect and often suffer from noises that may affect interpretations of the data, the models created from the data, and the decisions made based on the data. A common solution for handling noise is to employ outlier detection techniques. LOF is a well-known and widely used algorithm for outlier detection based on local densities of data. However, it does not perform well on removing class noises since it does not take the information of class labels into consideration. In this paper, we propose a new noise removal algorithm based on Combined Local Outlier Factors: CLOF. Specifically, CLOF firstly defines three local outlier factors, i.e., lofa, lof1} and lof0}, and eliminates attribute noises using lofa}. Then, CLOF finds and corrects the labels of class noises by simultaneously using the three local outlier factors. Experimental results on artificial and real-world UCI data sets demonstrate that CLOF can effectively identify class noises and attribute noises so as to improve the classification performances of various classifiers, especially for data sets with severe class overlappings.
	TO cite this article:REN Yi-li,WU Jun-jie,XIONG Hai-tao, et al. CLOF: A Noise Removal Algorithm Based on Combined Local Outlier Factors[J].

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	3. An image similarity measure based on corner
	Li Dehua,Zhu Jingchao,Yang Zhi
	Computer Science and Technology 07 January 2013
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	Abstract:This paper presents a method to compare an image with a set of images and rank the images by their similarity. The common way either uses time-cost features or a set of training examples to get arguments, this method is fast and similar with text comparison. Firstly, we get all images' corner points by chord-to-point distance accumulation (CPDA), then generate their feature vectors by corners which can overcome different lightness, rotation and scale, finally compute the similarity between the images by their feature vectors, while the similarity function provides two arguments to fit into different applications. The experimental results tell us that the precision and performance both perform very well. As this method is kind of like text comparison, so it is well-defined for the image search engine.
	TO cite this article:Li Dehua,Zhu Jingchao,Yang Zhi. An image similarity measure based on corner[OL].[ 7 January 2013] http://en.paper.edu.cn/en_releasepaper/content/4511424


	4. Frameworks for Multimodal Biometric using Sparse Representation
	Huang Zengxi,Liu Yiguang,Huang Ronggang,Yang Menglong
	Computer Science and Technology 04 January 2013
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	Abstract:This paper will introduce three frameworks of two fusion levels for multimodal biometric using sparse representation based classification (SRC), which has been successfully used in many classification tasks recently. The first framework is multimodal SRC at match score level (MSRC_s), in which feature of each modality is sparsely coded independently, and then their representation fidelities are used as match scores for multimodal classification. The other two frameworks are of multimodal SRC at feature level, namely MSRC_f1 and MSRC_f2, where features of all modalities are first fused and then classified by using SRC. The difference between them is that MSRC_f1 fuses the features to form a unique multimodal feature vector, while MSRC_f2 implicitly combines the features in an iterative joint sparse coding process. As a typical application, the fusion of face and ear for human identification is investigated by using the three frameworks. Many results demonstrate that the proposed multimodal methods are significantly better than the multimodal recognition using common classifiers. Among the SRC based methods, MSRC_s gets the top recognition accuracy in almost all the test items, which might benefit from allowing sparse coding independence for different modalities.
	TO cite this article:Huang Zengxi,Liu Yiguang,Huang Ronggang, et al. Frameworks for Multimodal Biometric using Sparse Representation[OL].[ 4 January 2013] http://en.paper.edu.cn/en_releasepaper/content/4511813


	5. Blockwise Coordinate Descent Schemes for Effective Dictionary Learning
	Liu Baodi,ZhangYujin
	Computer Science and Technology 29 November 2012
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	Abstract:Sparse coding, which is usually viewed as a method for rearranging the structure of the original data in order tomake the energy compact over non-orthogonal and overcomplete dictionary, is widely used in signal processing, pattern recognition, machine learning, statistics, and neuroscience. Unfortunately, finding sparse codes and learning bases remain computationally difficult up to now, and the performance of sparse coding is sensitive to the learned dictionary. In this paper, we propose a blockwise coordinate descent algorithm with guaranteed convergence to solve these two problems under a unified scheme. The variables involved in the optimization problems are partitioned into several suitable blocks with convexity preserved, making it possible to perform an exact block coordinate descent. For each separable subproblem, based on the convexity and monotonic property of the parabolic function, a closed-form solution is obtained. Thus the algorithm is simple, efficient and effective. Experimental results show that our algorithm not only significantly accelerates the learning process, but also greatly helps improve the performance of real applications.
	TO cite this article:Liu Baodi,ZhangYujin. Blockwise Coordinate Descent Schemes for Effective Dictionary Learning[OL].[29 November 2012] http://en.paper.edu.cn/en_releasepaper/content/4498604


	6. Selecting Effective and Discriminative Spatio-Temporal Interest Points for Recognizing Human Action
	ZHANG Hongbo,LI Shaozi,SU Songzhi
	Computer Science and Technology 29 October 2012
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	Abstract:Many successful methods for recognizing human action are spatio-temporal interest point (STIP) based methods. Given a test video sequence, for matching-based method using voting mechanism, each test STIP casts a vote for each action class based on its mutual information with respect to the respective class, which is measured in terms of class likelihood probability. Therefore, two issues should be addressed to improve the accuracy of action recognition. First, effective STIPs in the training set must be selected as references for accurately estimating probability. Second, discriminative STIPs in test set must be selected for voting. This work uses e-nearest neighbors as effective STIPs for estimating the class probability and uses a variance filter for selecting discriminative STIPs. Experimental results verify that the proposed method is more accurate than existing action recognition methods.paper.
	TO cite this article:ZHANG Hongbo,LI Shaozi,SU Songzhi. Selecting Effective and Discriminative Spatio-Temporal Interest Points for Recognizing Human Action[OL].[29 October 2012] http://en.paper.edu.cn/en_releasepaper/content/4493196


	7. Design of a system for classifying the relationship based on video behavior analysis
	Wu Hao
	Computer Science and Technology 12 February 2012
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	Abstract:Moving object detection and video behavior analysis has become one of the hotspots in modern society. Analysis and hazard identification is also on the foundation of it.Through moving object detection and classification algorithm ,we can dig up more information from the video.We do further research on the foundation of prior research and adopt Pathfinding algorithm , Bayesian estimation to classify the relationship between two people.The system can be used in supermarket ,office and some important place .It also can be used for investigation work.At the same time,the system can help us collect information,can be widely used in market analysis ,can be used to get more commercial value.
	TO cite this article:Wu Hao. Design of a system for classifying the relationship based on video behavior analysis[OL].[12 February 2012] http://en.paper.edu.cn/en_releasepaper/content/4465989


	8. Automatic age estimation via sparse representation
	LIANG Yixiong,LIU Lingbo
	Computer Science and Technology 09 February 2012
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	Abstract:Automatic age estimation from face has received increasing attention due to its wide range of application. A successful age estimator typically consists of two key modules: age-related feature extraction and age estimation by regression or classification. In this paper we propose a novel age estimator method based on sparse representation. In the feature exaction stage, the mid-level Spatial-Pyramid face representation based on Sparse codes of SIFT features (ScSPM) is used to characterize the age-related variance. For age estimation, linear sparse regression models are learned which can not only select the most discriminative features but also perform the age estimation. The hierarchical strategy, which first coarsely classifies the faces into age groups and then finely estimates the detailed age by the linear regression model of this group, is adopted to deal with the non-linearity attribute of aging to improve the performance of the age regression model. To our best knowledge, it is the first time to apply ScSPM and sparse linear regression to age estimation. The experimental results show that the proposed approach outperforms the state-of-the-art on the FG-NET database and achieves competitive performance on the MORPH database.
	TO cite this article:LIANG Yixiong,LIU Lingbo. Automatic age estimation via sparse representation[OL].[ 9 February 2012] http://en.paper.edu.cn/en_releasepaper/content/4464426


	9. A New Gabor Method for Face Recognition
	He Lianghua
	Computer Science and Technology 18 January 2012
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	Abstract:Because of containing enough texture information around base point, Local Binary Pattern features and Gabor features shows excellent performance under batch light varying and rotation. However, the complicated calculation and feature’s high dimension are the biggest restriction in application. Therefore, in this paper we proposed a novel method called Binary Gabor Codes based on above two methods. The key idea is calculating local binary patterns on the corresponding Gabor Magnitude Pictures (GMPs), the calculation and dimension are both decreased. Because of containing both information of local texture and block gray varying, BGC features are more overwhelming comparing with Local Binary Patterns (LBP), Gabor jets and independent component features. What’s more, Experimental results show that it has improved the recognition rate greatly, especially in bad condition.
	TO cite this article:He Lianghua. A New Gabor Method for Face Recognition[OL].[18 January 2012] http://en.paper.edu.cn/en_releasepaper/content/4462906


	10. A Novel Shadow Detection Method Based on Color and Texture Features
	Li Qiaohong,Zhang Honggang,Gu Fang,Dai Yourui,Yu Jie
	Computer Science and Technology 27 December 2011
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	Abstract:This paper describes a new method for shadow detection of moving objects in video surveillance applications, improving the detection performance and fascinating the following image processing steps, such as object tracking, classification and behavior analysis. This method is based on the priori that shadow region and background region share the similar textural and chromatic characteristics. This method combines the virtues of color and texture features, to select the best criterion to discriminate shadow pixels from object pixels. Experiment shows that this method can achieve desirable performance under indoor and outdoor environment.
	TO cite this article:Li Qiaohong,Zhang Honggang,Gu Fang, et al. A Novel Shadow Detection Method Based on Color and Texture Features[OL].[27 December 2011] http://en.paper.edu.cn/en_releasepaper/content/4456940

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