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Efficient image representation for object recognition via pivots selection

Frontiers of Computer Science 2014年

作者： Xie, Bojun Liu, Yi Zhang, Hui Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well-known pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to "grow-up" features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive *** this paper, we present efficient kernel descriptor (EKD) and efficient hierarchical kernel descriptor (EHKD), which are built upon incomplete Cholesky decomposition. EKD automatically selects a small number of pivot features for generating patch-level features to achieve better computational efficiency. 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. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

关键词： Object recognition

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Detect the Rail Track Flaw by Estimating the Camera Shaking

Detect the Rail Track Flaw by Estimating the Camera Shaking

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IEEE International Conference on Intelligent transportation Systems

作者： Liang Wang Xiaoyue Luo Berthold K. P. Horn Shengchun Wang Siwei Luo Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Mathematics Linfield College OR USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Boston MA USA

In this paper, we present a method of detecting the rail track bends and flaws. Different from the traditional machine-vision based methods which analyze the images of the rail surface, we estimate the shake of the camera - which is caused by the flaw or bend of the rail track - from the video used for railway environment surveillance. We provide both theoretical analysis - based on the theory of optical flow and passive navigation - and the corresponding fast algorithms, i.e. the brightness-pattern-matching code (BPMC) and shaking-type code (STC). Our method uses the surveillance video of the railway environment directly, thus, we do not need extra expensive instruments.

关键词： Rails Cameras Brightness Optical imaging Algorithm design and analysis Turning Navigation

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A self-adaptive iterated local search algorithm on the permutation flow shop scheduling problem

A self-adaptive iterated local search algorithm on the permu...

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International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Xingye Dong Maciek Nowak Ping Chen Youfang Lin Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Quinlan School of Business Loyola University Chicago IL U.S.A TEDA College NanKai University Tianjin China

Iterated local search (ILS) is a simple, effective and efficient metaheuristic, displaying strong performance on the permutation flow shop scheduling problem minimizing total flow time. Its perturbation method plays an important role in practice. However, in ILS, current methodology does not use an evaluation of the search status to adjust the perturbation strength. In this work, a method is proposed that evaluates the neighborhoods around the local optimum and adjusts the perturbation strength according to this evaluation using a technique derived from simulated-annealing. Basically, if the neighboring solutions are considerably worse than the best solution found so far, indicating that it is hard to escape from the local optimum, then the perturbation strength is likely to increase. A self-adaptive ILS named SAILS is proposed by incorporating this perturbation strategy. Experimental results on benchmark instances show that the proposed perturbation strategy is effective and SAILS performs better than three state of the art algorithms.

关键词： Algorithm design and analysis Perturbation methods Search problems Educational institutions Benchmark testing Job shop scheduling Convergence

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Development of large-scale TCM corpus using hybrid named entity recognition methods for clinical phenotype detection: An initial study

Development of large-scale TCM corpus using hybrid named ent...

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IEEE Symposium on Computational Intelligence in Big data (CIBD)

作者： Lizhi Feng Xuezhong Zhou Haixun Qi Runshun Zhang Yinghui Wang Baoyan Liu School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Guang'anmen Hospital China Academy of Chinese Medicine Sciences Beijing China China Academy of Chinese Medicine Sciences Beijing China

ISBN: (纸本)9781479945399

Clinical data is one of the core data repositories in traditional Chinese medicine (TCM) because TCM is a clinically based medicine. However, most clinical data like electronic medical record in TCM is still in free text. Due to the lack of large-scale annotation corpus in TCM field, in this paper, we aim to develop an annotation system for TCM clinical text corpus. To reduce the manual labors, we implement three named entity recognition methods like supervised machine learning method, unsupervised method and structured data comparison, to assist the batch annotations of clinical records before manual checking. We developed the system using Java and have curated more than 2,000 records of chief complaint in an effective way.

关键词： Medical diagnostic imaging Manuals Hidden Markov models Standards Training data mining databases

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Recommending scientific articles using bi-relational graph-based iterative RWR 13

Recommending scientific articles using bi-relational graph-b...

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7th ACM Conference on Recommender Systems, RecSys 2013

作者： Tian, Geng Jing, Liping Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University China

ISBN: (纸本)9781450324090

The overabundance of scientific article information has created much inconvenience to researchers seeking interesting articles online. In this paper, we provide a Bi-Relational graph to represent the heterogenous information of scientific article recommendation system, which includes three parts: the article content similarity, researcher interest correlation, and researcher-article readership. Meanwhile, an iterative random walk with restarts learning method is proposed on the Bi-Relational graph to recommend a researcher rating for each article by making use of the known information. The proposed method has ability to perform both old and new article recommendation. A series of experiments on CiteULike dataset have shown that our method is more effective than other testing methods in the paper. © 2013 ACM.

关键词： Graphic methods

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Detect the Rail Track Flaw by Estimating the Camera Shaking

Detect the Rail Track Flaw by Estimating the Camera Shaking

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17th International IEEE Conference on Intelligent transportation Systems & The Asia-Pacific Council on Systems Engineering Conference 2014

作者： Liang Wang Xiaoyue Luo Berthold K.P.Horn Shengchun Wang Siwei Luo Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Department of Mathematics Linfield CollegeOR 97128USA CSAIL Department of Electrical Engineering and Computer ScienceMassachusetts Institute of TechnologyBostonMA02139 USA

In this paper,we present a method of detecting the rail track bends and *** from the traditional machine-vision based methods which analyze the images of the rail surface,we estimate the shake of the camera — which is caused by the flaw or bend of the rail track — from the video used for railway environment *** provide both theoretical analysis — based on the theory of optical flow and passive navigation — and the corresponding fast algorithms,*** brightness-pattern-matching code(BPMC) and shaking-type code(STC).Our method uses the surveillance video of the railway environment directly,thus,we do not need extra expensive instruments.

关键词： image Detect the Rail Track Flaw by Estimating the Camera Shaking Track

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A parallel implementation of TLD algorithm using CUDA

A parallel implementation of TLD algorithm using CUDA

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5th IET International Conference on Wireless, Mobile and Multimedia Networks, ICWMMN 2013

作者： Zhang, Ping Sun, Yongqi Wu, Yali Zhang, Rui Beijing Key Lab. of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China

ISBN: (纸本)9781849197267

Visual tracking is an important issue of computer vision, TLD is an on-line visual tracking algorithm with good robustness and high accuracy properties. However, the real-time performance of TLD is low for the large size video sequences. In this paper, we study the most time-consuming stages of TLD, and then propose a parallel algorithm based on CUDA. The experimental results show that the speedup of our algorithm reaches up to 2.59 compared to TLD while maintaining the same detection accuracy.

关键词： Graphics processing unit

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Site-based and resource-constrained project scheduling problem: Model and heuristics

Site-based and resource-constrained project scheduling probl...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Yuan-Li. Zheng Yong-Liang. Luo You-Fang. Lin Zheng-Sheng. Wei Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China SYSTEMS ENGINEERING RESEARCH INSTITUTE Beijing China

In this paper, we present a site-based and resource-constrained project scheduling problem with generalized precedence relation (SRCPSP-GPR) by extending the RCPSP-GPR, in which the site constraints are considered to minimize project durations. After formalizing the SRCPSP-GPR, we give three concepts, i.e., Same-site activities, SubNet and Combined-SubNet, based on the precedence and site constraints. Then, we propose two heuristic algorithms to resolve this problem, the SubNet based search (SNBS) algorithm and the Combined-SubnNet based search (CSNBS) algorithm. The experimental results show that CSNBS is much more effective than SNBS, i.e., it can get more minimum project durations. However, the computational complexity of CSNBS is obviously larger than that of SNBS.

关键词： Tin Processor scheduling Heuristic algorithms Scheduling Wireless sensor networks Time factors Schedules

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Physical Review E 2013年第1期88卷 012807-012807页

作者： Bian-fang Chai Jian Yu Cai-yan Jia Tian-bao Yang Ya-wen Jiang Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing 100044 China Department of Information Engineering Shijiazhuang University of Economics Hebei 050031China GE Global Research San Ramon California 94583 USA

Latent community discovery that combines links and contents of a text-associated network has drawn more attention with the advance of social media. Most of the previous studies aim at detecting densely connected communities and are not able to identify general structures, e.g., bipartite structure. Several variants based on the stochastic block model are more flexible for exploring general structures by introducing link probabilities between communities. However, these variants cannot identify the degree distributions of real networks due to a lack of modeling of the differences among nodes, and they are not suitable for discovering communities in text-associated networks because they ignore the contents of nodes. In this paper, we propose a popularity-productivity stochastic block (PPSB) model by introducing two random variables, popularity and productivity, to model the differences among nodes in receiving links and producing links, respectively. This model has the flexibility of existing stochastic block models in discovering general community structures and inherits the richness of previous models that also exploit popularity and productivity in modeling the real scale-free networks with power law degree distributions. To incorporate the contents in text-associated networks, we propose a combined model which combines the PPSB model with a discriminative model that models the community memberships of nodes by their contents. We then develop expectation-maximization (EM) algorithms to infer the parameters in the two models. Experiments on synthetic and real networks have demonstrated that the proposed models can yield better performances than previous models, especially on networks with general structures.

关键词： COMMUNITY organization POPULARITY SOCIAL media PROBABILITY theory STOCHASTIC models ALGORITHMS PERFORMANCE evaluation

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Frequent itemsets compressing based on minimum cover: An efficient method for mining medication law of Chinese herbs

Frequent itemsets compressing based on minimum cover: An eff...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lei Zhang Yiguo Wang Qiming Zhang Xuezhong Zhou Jian Yu Xiuhua Guo Xia Li China Academy of Chinese Institute of Basic Research in Clinical Medicine Beijing China College of Computer Science and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing China School of Public Health Municipal Key Laboratory of Clinical Epidemiology Beijing China

ISBN: (纸本)9781479913114

Frequent itemsets mining is often used to find medication law from dataset of Chinese herb prescriptions. Threshold of support count is difficult to set for traditional algorithm of frequent itemsets mining. In the meantime, the number of frequent itemsets is always so big that the result is hard to understand. Some algorithms were proposed to find significant and redundant-aware itemsets. However, the itemsets obtained could not reflect all the information in the dataset. In this paper, a new method was proposed to obtain a collection of itemsets which had the feature of significant, redundant-aware and comprehensive. Firstly, closed frequent itemsets were mined from the dataset of Chinese herbs prescriptions using CHARM algorithm. Then, the itemsets were compressed by FICMC (Frequent Itemsets Compressing based on Minimum Cover) algorithm. Medication law of Chinese herbs could be fully mined from the dataset using this method.

关键词： Itemsets Medical diagnostic imaging Educational institutions Algorithm design and analysis Association rules

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