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	1. ENTITY RANKING BASED ON DOCUMENT CENTERED MODEL
	Ouyang Haoyi,Xu Weiran
	Computer Science and Technology 29 November 2011
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	Abstract:The traditional search engines answer user's question by returning a collection of documents relevant to their query. However many user information needs would be better answered by specific entities instead of just any type of documents. This paper introduces a method that ranks a given list of entities according to their relevance to query. We use a method called Document Centered Model. Research models including BM25, KL-divergence have been tested to make the result better. Our criteria are from TREC Entity 2010.
	TO cite this article:Ouyang Haoyi,Xu Weiran. ENTITY RANKING BASED ON DOCUMENT CENTERED MODEL[OL].[29 November 2011] http://en.paper.edu.cn/en_releasepaper/content/4452555


	2. A Multi-feature Descriptor for E-Commodity Image Retrieval
	Yu Jie,Zhang Honggang,Li Qiaohong,Chai Lunshao,Qi Yonggang
	Computer Science and Technology 13 October 2011
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	Abstract:With the development of the 3G network and Electronic Commerce, more and more people prefer to shop online, especially via the Cell phone equipped with camera. In order to meet the requirements of the consumers to easily retrieve similar commodity images, we propose a Mobile Image-to-Search system. In this system a content-based retrieval algorithm combining color and shape features for electronic commerce has been investigated, which uses the Normalized Fourier Descriptor and a fuzzy-linking method of color histogram based on the HSV color space. In order to reduce the interference of background, we also use the method of improved canny descriptor before extracting the shape features and the approach of ROI (region of interest) weighted in the color histogram. The system has been tested on Corel data set and our own commodity library, a large number of images selected from Taobao, one of the largest Electronic Commerce web in China. The experimental results indicate that our method has a better retrieval performance.
	TO cite this article:Yu Jie,Zhang Honggang,Li Qiaohong, et al. A Multi-feature Descriptor for E-Commodity Image Retrieval[OL].[13 October 2011] http://en.paper.edu.cn/en_releasepaper/content/4444419

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	3. Head Pose Estimation based on the Mahanalobis Sparse Representation Classifier
	MA Bingpeng,PANG Xiumei,HU Weijun
	Computer Science and Technology 05 August 2011
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	Abstract:In this paper, a novel method is presented to improve the accuracy of head pose estimation. For the classify in head pose, the Mahanalobis Sparse Representation Classifier~(MSRC) method is proposed to improve the ability of classify. By using the Mahanalobis distance of samples belongs to the different classes, MSRC improve the classify ability from reducing the reconstructed error of the testing samples. The combination of MSRC can improve the accuracy of head pose estimation greatly. To show the effectiveness of MSRC, we compared them with other methods under two different databases. The results of the experiments show the proposed methods can improve the accuracy of head pose estimation.
	TO cite this article:MA Bingpeng,PANG Xiumei,HU Weijun. Head Pose Estimation based on the Mahanalobis Sparse Representation Classifier[OL].[ 5 August 2011] http://en.paper.edu.cn/en_releasepaper/content/4437887


	4. Research on Fault Diagnosis System of Mine Ventilator Based on Elman Neural Network
	Ren Zihui,Li Jiangang,Liu Yanxia
	Computer Science and Technology 28 June 2011
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	Abstract:This paper introduced the theory, learning algorithm and technical route of Elman neural. Though acquainting fault signals on-site and normalizing characteristic data, this method realized intelligent diagnosis of ventilator by constructing optimum structure and parameters based on Elman neural network. Compared with the traditional BP neural network, Elman network had a better comprehensive performance in diagnosis of ventilator. The result for the fault diagnosis of a ventilator showed that the Elman network improves the study speed, represses the network to sink local minimum, shortens the study time, and Elman neural is a effective method for the fault diagnosis of ventilator.
	TO cite this article:Ren Zihui,Li Jiangang,Liu Yanxia. Research on Fault Diagnosis System of Mine Ventilator Based on Elman Neural Network[OL].[28 June 2011] http://en.paper.edu.cn/en_releasepaper/content/4433667


	5. Face Extraction from Video Sequences by K-means Clustering and Fusion
	JI Luping
	Computer Science and Technology 25 May 2011
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	Abstract:Face segmentation is an important processing step in a typically face-based person identification system. This paper presents a modified K-means clustering and fusion approach for face region extraction from colorful images of video sequences. It is based on Lab* color space analysis and consists of three processing steps: color number estimation, color region clustering, and face region post-processing. Moreover, experimental results on CIPR public video sequences are also exhibited to verify the feasibility, high efficiency and accuracy.
	TO cite this article:JI Luping. Face Extraction from Video Sequences by K-means Clustering and Fusion[OL].[25 May 2011] http://en.paper.edu.cn/en_releasepaper/content/4429836


	6. A New Method for Clustering Ensembles
	YANG Lili,YU Jian,JIA Caiyan
	Computer Science and Technology 11 March 2011
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	Abstract:In recent years, clustering ensembles have attracted much attention since they outperforms the traditional clustering methods. A lot of researches have been done both on constructing the individual partitions and on designing the consensus functions. This paper focuses on the second aspect. Namely, how to combine the multiple data partitions to get a consistent partition for a given dataset using the information obtained in the different clusterings. In this paper, we propose a new method of combining multiple partitions by using the Squared Error Adjacent Matrix (SEAM) algorithm. We conducted several experiments of the proposed method both on the synthetic and the real-world datasets and compared the method with the graph-based consensus functions, CSPA, HGPA, and MCLA. Experimental results show that the proposed method is better or comparable to the graph-based methods.
	TO cite this article:YANG Lili,YU Jian,JIA Caiyan. A New Method for Clustering Ensembles[OL].[11 March 2011] http://en.paper.edu.cn/en_releasepaper/content/4415772


	7. Shape retrieval based on triangle measurement in multiple scales
	WANG Junwei,LIU Wenyu
	Computer Science and Technology 11 January 2011
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	Abstract:This paper proposed a novel descriptor, called multi-scale triangle measurement (MSTM) for shape retrieval. The original shape is represented by a serious of uniform sample points, and each sample point is described by geometric measurement (rotational angle and side lengths) in different scale levels based on some triangles that consist of this sample point and its adjacent critical points, which are abstracted with polygonal approximation approaches. The generated triangles investigate the local variance (e.g. local deformation) and global information (e.g. topology) among different scale levels. After computing MSTM, the usual dynamic programming (DP) technique is employed based on the uniform sample points. The novel descriptor was applied on two well-known datasets: MPEG-7 and Tari1000. Experiments show that our descriptor achieves a retrieval rate comparable to state-of-the-art on the MPEG-7 data set, and outperforms other algorithms on the Tari1000 data set. Our descriptor has less consumption in feature computing than certain classical descriptors, e.g., the Inner Distance Shape Context (IDSC).
	TO cite this article:WANG Junwei,LIU Wenyu. Shape retrieval based on triangle measurement in multiple scales[OL].[11 January 2011] http://en.paper.edu.cn/en_releasepaper/content/4405596


	8. Equivalence Between 2DPCA-L1 and PCA-L1
	Wang Haixian
	Computer Science and Technology 05 January 2011
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	Abstract:Principal component analysis (PCA), as one of the most popular unsupervised dimensionality reduction methods, is of importance in multivariate data analysis. It seeks a set of orthogonal bases such that the variance of the input data points is maximized. The conventional PCA, however, is sensitive to outliers due to the utilization of L2-norm. As a robust alternative to PCA, PCA-L1 is proposed in literature. In image domain, two-dimensional PCA (2DPCA) is directly based on image matrices, obviating the image-to-vector transformation as in PCA. Likewise, 2DPCA uses L2-norm, and 2DPCA-L1, proposed in literature, is the robust version of 2DPCA. PCA-L1 and 2DPCA-L1 are two important subspace learning approaches developed recently. In this paper, we show that 2DPCA-L1 is in fact a special case of PCA-L1 applying to row vectors of image matrices. Thus, the relationship between these two methods is made clear.
	TO cite this article:Wang Haixian. Equivalence Between 2DPCA-L1 and PCA-L1[OL].[ 5 January 2011] http://en.paper.edu.cn/en_releasepaper/content/4404185


	9. Research on Human and Machine Performance of Handwritten Chinese Character Recognition in HCL2000
	Wan Xinxin,Zhang Honggang
	Computer Science and Technology 02 December 2010
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	Abstract:In this paper, human performance on handwritten Chinese character recognition is compared to machine, which aims to obtain the required accuracy for further handwritten word segmentation and recognition. HCL2000, one of the largest databases of handwritten Chinese characters, introduces sample characters into the performance evaluation. A system of Human Performance Test on HCL2000 is designed to examine the accuracy of human recognition. According to the experiment results, the best machine record is competitive with average human performance. LPP and MFA employing the gradient feature vectors of size 512 far outperform LDA on the same dimensionality.
	TO cite this article:Wan Xinxin,Zhang Honggang. Research on Human and Machine Performance of Handwritten Chinese Character Recognition in HCL2000[OL].[ 2 December 2010] http://en.paper.edu.cn/en_releasepaper/content/4394583


	10. Combining Extra Information into Topic Models
	TANG Shoucheng
	Computer Science and Technology 26 November 2010
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	Abstract:Statistical topic models are attractive because they allow for a rapid analysis and understanding of new collections of text. However, this framework cannot provide sufficient information for the problem of learning a topic hierarchy from data. It has been shown recently that the data-driven learning approaches combined with some structure and prior knowledge can be a satisfactory solution. In this paper, we review a new probabilistic framework which adds the hierarchical information within document frequency into topics to seek the more semantic structure. The hierarchical topics created by DF topic model have a natural relationship beyond the tree structure. We illustrate our approach on 20 Newsgroups to show the performance of our model in extracting hierarchy of topics.
	TO cite this article:TANG Shoucheng. Combining Extra Information into Topic Models[OL].[26 November 2010] http://en.paper.edu.cn/en_releasepaper/content/4393355

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