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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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Research on SVM and FLDA in classification with comparative experiments

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

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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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Environment coupled metrics learning for unconstrained face verification

Environment coupled metrics learning for unconstrained face ...

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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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引用

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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Human Action Recognition with Attribute Regularization

Human Action Recognition with Attribute Regularization

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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 ...

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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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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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Heuristic dynamic programming-based H∞ control of discrete-time affine nonlinear systems

Heuristic dynamic programming-based H∞ control of discrete-...

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International Conference on Intelligent control and Information Processing (ICICIP)

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

In this paper, we solve the H ∞ control problems for discrete-time affine nonlinear systems with known dynamics. An iterative heuristic dynamic programming algorithm is derived and the convergence analysis is provided. Three neural networks are used to approximate the control policy, the disturbance policy, and the value function, respectively. A simulation example is presented to demonstrate the effectiveness of the proposed scheme.

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Contextual Fisher kernels for human action recognition

Contextual Fisher kernels for human action recognition

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International Conference on Pattern Recognition

In the literature of human action recognition, despite promising results have been obtained by the traditional bag-of-words model, the relationship among spatiotemporal points has rarely been considered. Furthermore, serious quantization error also exists in this kind of strategy. In this paper, we propose a novel coding strategy named contextual Fisher kernels to overcome these limitations. We add a Gaussian function to represent contextual information into the generative model. In this way, our method explicitly considers the spatio-temporal contextual relationships between interest points and alleviates quantization error. Our method is verified on two challenging datasets (KTH and UCF sports), and the experimental results demonstrate that our method achieves better results than the state-of-the-art methods in human action recognition.

关键词： Kernel Humans Context modeling Quantization Vectors Accuracy Covariance matrix

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