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Human action recognition by bagging data dependent representation

Human action recognition by bagging data dependent represent...

引用

International Conference on Pattern Recognition

作者： Wen Zhou Chunheng Wang Baihua Xiao Zhong Zhang Long Ma State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781467322164

Traditional methods based on bag-of-word representation are easily affected by noise, and they also cannot handle the problem when a test distribution differs from the training distribution. In this paper, we propose a novel method for human action recognition by bagging data dependent representation. Different with traditional methods, the proposed method represents each video by several histograms. These histograms are obtained by bagging according to an estimated prior several times in both training and testing. The data dependent property of our method depends on the prior which reflects the training distribution. There are two advantages of the proposed method. First, it alleviates the distribution difference between training set and test set. Second, the bagging operation reduces noise and improves the performance significantly. Experimental results show the effectiveness of the proposed method.

关键词： Data structures Boolean functions Bagging Humans Training Accuracy Noise

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Artificial Transportation system constrution based on ontology-based knowledge base systems

Artificial Transportation System constrution based on ontolo...

引用

2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

作者： Chen, Songhang Zhu, Fenghua State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songs han Lake Dongguan China

ISBN: (纸本)9781467324007

Artificial Transportation systems (ATS) provide a comprehensive perspective to study actual transportation systems, which are a kind of open and complex giant system referring to diverse engineering and social disciplines. To make use of achievements from related research fields, ontology for ATS is constructed in this paper. Besides conceptual model involved in modeling and applications of ATS, the compatibility with other ontology is considered. Further, an ontological knowledge base with SOA (Service-Oriented Architecture) is designed to manage information and knowledge in ATS. The architecture and interfaces of the knowledge base are presented. Benefit from this, construction and applications of ATS can be conducted on a uniform information and knowledge platform. A case study about traffic jam pattern is demonstrated. © 2012 IEEE.

关键词： Ontology

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Composite service execution Petri Net and service composition optimization

Composite service execution Petri Net and service compositio...

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2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

作者： Liu, Sheng Liu, Xiwei Zhao, Hongxia Fu, Wei State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing 100190 China School of International Trade and Economics University of International Business and Economics Beijing 100029 China

ISBN: (纸本)9781467324007

Web services are becoming the most promising technology for cloud computing. When a single web service fails to satisfy service requestor's multiple function demands, web services need to be configured together to form a service composition with cost and other constraints. It is hard to obtain an optimal service composition by the existing service composition methods because the analysis of its dynamic execution process is neglected. This paper uses Petri Net to model and analyze the dynamic execution processes of service compositions. The effect of composite services reliability on the composite service performance is taken into account. The method regards the cost averaged for one time of successful execution of a composite as its actual executing cost, and then selects the composite services with the aim of minimizing the composite service execution cost. The simulation result shows that the proposed method is superior to other methods in execution cost. © 2012 IEEE.

关键词： Quality of service

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Research on SVM and FLDA in classification with comparative experiments

Research on SVM and FLDA in classification with comparative ...

引用

2012 9th IEEE International Conference on Networking, Sensing and control, ICNSC 2012

作者： Qian, Yegan Xiong, Gang Yao, Yanjie Anhui Radio TV Station and Hefei Hanteng Info. Tech. Company Anhui Hefei 230022 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467303880

The paper discusses two important classification techniques, Fisher's linear discriminated analysis (FLDA) and Support Vector Machine (SVM). First, we propose a theoretical discussion, and then implement FLDA and SVM on several datasets of two classes and multiclass, a comparative experimental analysis among these two techniques aims at exploring and assessing the performance of FLDA and SVM classifiers. To sustain such analysis, the two classification techniques are compared with different training data sets and testing data sets. Different performance indicators have been used to support our experimental studies in a detailed and accurate way such as the classification accuracy. The results obtained on different datasets conclude that FLDA and SVM are valid and effective approaches for pattern classification and conclude their different performance and problems with different size datasets. Meanwhile, the paper employs a non-traditional method to get the training and testing data set, and concludes detailed pros and cons from the experiment results. © 2012 IEEE.

关键词： Support vector machines

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International Joint Conference on Neural Networks (IJCNN)

作者： Shuangyong Song Qiudan Li Xiaolong Zheng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The rapid increasing popularity of micro-blogging has made it an important information seeking channel. By detecting recent popular topics from micro-blogging, we have opportunities to gain insights into internet hotspots. Generally, a topic's popularity is determined by two primary factors. One is how frequently a topic is discussed by users, and the other is how much influence those users have, since topics shown in the influential users' posts are more likely to attract others' attention. However, existing approaches interpret a topic's popularity with only the number of keywords related to it, which neglect the importance of the user influence to information diffusion in micro-blogging. In this paper, drawing upon the Cognitive Authority Theory and Social Network Theory, we propose a novel model that detects the most popular topics in micro-blogging with a user interest-based method. The proposed model first constructs a topic graph according to users' interests and their following relationship, and then calculates the topics' popularity with a link-based ranking algorithm. The popular topics detected by the method can reflect the relationship among users' interests, and the topics in the posts of influential users can be highlighted. Experimental results on the data of Twitter, a well-known and feature-rich micro-blogging service, show that the proposed method is effective in popular topic discovery.

关键词： Twitter Blogs Algorithm design and analysis Navigation Vectors Semantics

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Environment coupled metrics learning for unconstrained face verification

Environment coupled metrics learning for unconstrained face ...

引用

IEEE International Conference on Image Processing

作者： Xinyuan. Cai Chunheng. Wang Baihua. Xiao Ji. Zhou Xue. Chen State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Making recognition more reliable under unconstrained environment is one of the most important challenges for realworld face recognition. In this paper, we propose a novel approach for unconstrained face verification. First, we use a spectral-clustering method based on Structural Similarity index to estimate the captured environments of facial images. Then for each pair of environments, we learn two coupled metrics, such that facial images captured in different environments can be transformed into a media subspace, and high recognition performance can be achieved. The coupled transformations are jointly determined by solving an optimization problem in the multi-task learning framework. Experimental results on the benchmark dataset (LFW) show the effectiveness of the proposed method in face verification across varying environments.

关键词： Measurement Face Face recognition Lighting Training Indexes Benchmark testing

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Sparse representation for face recognition based on discriminative low-rank dictionary learning

Sparse representation for face recognition based on discrimi...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Long Ma Chunheng Wang Baihua Xiao Wen Zhou State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm.

关键词： Dictionaries Strontium Noise Training Sparse matrices Face Encoding

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Illumination-invariant completed LTP descriptor for cloud classification

Illumination-invariant completed LTP descriptor for cloud cl...

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Congress on Image and Signal Processing, CISP

作者： Shuang Liu Chunheng Wang Baihua Xiao Zhong Zhang Yunxue Shao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Cloud classification plays an essential role in a large number of applications. However, this issue is particularly challenging due to the extreme appearance variations under different atmospheric conditions. In this paper, a novel descriptor named illumination-invariant completed local ternary pattern (ICLTP) is proposed for cloud classification. The proposed descriptor effectively handles the illumination variations by introducing illumination invariant factor. Furthermore, the Quadratic-Chi metric, which is more suitable for comparing the difference between two histograms, is applied instead of Chi-Square metric. The experimental results demonstrate the superior performance of our strategy on two challenging cloud databases. Besides cloud classification, we further validate the proposed ICLTP operator on traditional texture classification, which show the good generalization ability.

关键词： Lighting Databases Measurement Clouds Histograms Pattern recognition Robustness

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Human Action Recognition with Attribute Regularization

Human Action Recognition with Attribute Regularization

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Zhong Zhang Chunheng Wang Baihua Xiao Wen Zhou Shuang Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this paper, we explicitly consider such attribute-object relationship, and correspondingly, we modify the multi-task learning model by adding attribute regularization. In this way, the learned model not only shares the low-level features, but also gets regularized according to the semantic constrains. Our method is verified on two challenging datasets (KTH and Olympic Sports), and the experimental results demonstrate that our method achieves better results than previous methods in human action recognition.

关键词： Humans Semantics Optimization Histograms Prediction algorithms Accuracy Training

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Adaptive optimal control for a class of nonlinear partially uncertain dynamic systems via policy iteration

Adaptive optimal control for a class of nonlinear partially ...

引用

International Conference on Intelligent control and Information Processing (ICICIP)

作者： Derong Liu Xiong Yang Hongliang Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In this paper, by employing an online algorithm based on policy iteration (PI), an adaptive optimal control problem for continuous-time (CT) nonlinear partially uncertain dynamic systems is investigated. In this proposed algorithm, a discounted cost function is discussed, which is considered to be a more general case for optimal control problems. Two neural networks (NNs) are used to implement the algorithm, which aims at approximating the cost function and the control law, respectively. The uniform convergence to the optimal control is proven, and the stability of the system is guaranteed. An illustrating example is given.

关键词： Optimal control Nonlinear systems Cost function Convergence Heuristic algorithms Equations Dynamic programming

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