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Stereovision-Only Based Interactive Mobile Robot for Human-Robot Face-to-Face Interaction

Stereovision-Only Based Interactive Mobile Robot for Human-R...

引用

international conference on Pattern Recognition

作者： Lei Chen Zhen Dong Sheng Gao Baofeng Yuan Mingtao Pei Beijing Institute of Spacecraft System Engineering Beijing P.R. China Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing P.R. China

ISBN: (纸本)9781479952106

In this paper, we present a stereovision-only based interactive mobile robot for supporting human-robot face-to-face interaction in the real world. A three-level architecture, which consists of sensor level, perception level and behavior level, is designed for the robot in order to perceive, understand and react to the human activity during interaction based only on visual information. A high performance stand-alone stereovision system (RGBD imager), developed in our lab, is applied to obtain the composite of color (RGB) images and dense disparity (D) maps at video rate. The RGBD imager allows the robot a human-like 3-D visual perception ability to (1) autonomously detect the human of interest whom the robot could interact with using the offline learning approaches, and (2) focus exclusively on the target human while both the human and the robot are moving during interaction using on-line learning approaches. We demonstrate and evaluate the performance of our interactive mobile robot in an office environment. The experimental results show that a reliable and dynamic face-to-face interaction is achieved, so that the target human face is always kept in the field of view and at a suitable social distance from the robot.

关键词： Robot sensing systems Skin Mobile robots Visualization Detectors Image color analysis

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Active Semi-supervised learning Using Optimum-Path Forest

Active Semi-supervised Learning Using Optimum-Path Forest

引用

international conference on Pattern Recognition

作者： Priscila T.M. Saito Willian P. Amorim Alexandre X. Falcao Pedro J. De Rezende Celso T.N. Suzuki Jancarlo F. Gomes Marcelo H. De Carvalho Dept. of Computer Engineering Federal Technological University of Parana PR Brazil Institute of Computing University of Campinas SP Brazil Institute of Computing Federal University of Mato Grosso do Sul MS Brazil Inst. of Comput. Universidade Estadual de Campinas Campinas SP BR IMMUNOCAMP Research and Develonment of Technoloug SP Bazil Institute of Biology University of Campinas Brazil SP R&D of Technol. Brazil

ISBN: (纸本)9781479952106

The development of effective and efficient ways of handling real-world applications is becoming increasingly widespread, yet it still faces a number of practical challenges. First and foremost, we have the limited availability of labeled data in contrast to an unbounded number of unlabeled ones. Despite some efforts in active semi-supervised learning, their success depends on an approach suitable to be applied to real massive data. In this paper, we introduce a novel integration of semi-supervised learning and a priori-reduction and organization criteria for active learning based on Optimum-Path Forest classifiers. Encouraging results on both public and real data show the synergy of these strategies jointly. Our approach iteratively generates semi-supervised classifiers that attain high accuracy by selecting the most representative labeled set, while decreasing the propagated errors on the unlabeled set. In addition, it is able to identify samples from all classes quickly while keeping user interaction to a minimum throughout the learning iterations.

关键词： Training Accuracy Semisupervised learning Prototypes Educational institutions Support vector machines Supervised learning

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Correspondence-Free Dictionary learning for Cross-View Action Recognition

Correspondence-Free Dictionary Learning for Cross-View Actio...

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international conference on Pattern Recognition

作者： Fan Zhu Ling Shao Department of Electronic and Electrical Engineering The University of Sheffield Sheffield UK

ISBN: (纸本)9781479952106

In this paper, we propose a novel unsupervised approach to enabling sparse action representations for the cross-view action recognition scenario. Superior to previous cross-view action recognition methods, neither target view label information nor correspondence annotations are required in this approach. Low-level dense trajectory action features are first coded according to their feature space localities within the same view by projecting each descriptor into its local-coordinate system under the locality constraints. Actions across each pair of views are additionally decomposed to sparse linear combinations of basis atoms, a.k.a., dictionary elements, which are learned to reconstruct the original data while simultaneously forcing similar actions to have identical representations in an unsupervised manner. Consequently, cross-view knowledge is retained through the learned basis atoms, so that high-level representations of actions from both views can be considered to possess the same data distribution and can be directly fed into the classifier. The proposed approach achieves improved performance compared to state-of-the-art methods on the multi-view IXMAS data set, and leads to a new experimental setting that is closer to real-world applications.

关键词： Dictionaries Cameras Encoding Equations Trajectory Feature extraction Optimization

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Transformed Neighborhood Propagation

Transformed Neighborhood Propagation

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international conference on Pattern Recognition

作者： Zhao Zhang Fan-Zhang Li Mingbo Zhao School of Computer Science and Technology Soochow University Suzhou P. R. China Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781479952106

An enhanced label propagation technique termed transformed neighborhood propagation (TNP) is proposed for semi-supervised learning. In the TNP setting, the processes of constructing weighted similarity graph and propagating label information of the labeled data to unlabeled points are conducted in the transformed feature space. TNP is mainly motivated by a fact that the optimal feature representation Y with possible unfavorable features and noises in the original data X removed by feature learning are more appropriate and accurate for measuring pair wise similarities of samples. To achieve the representation Y, The recent marginal semi-supervised sub-manifold projections is applied, so enhanced inter-class separation and enhanced intra-class compactness are delivered at the same time. The similarity graph is finally constructed based on Y. We also propose to calculate semi-supervised reconstruction weights for the weight assignment. As a result, the label estimation power can be enhanced by benefiting from the refined weighted similarity graph over Y instead of X, through propagating the labels of points in the transformed space for prediction. Visualization and image classification verified the effectiveness of our TNP, compared with other related label propagation algorithms.

关键词： Noise Image reconstruction Manifolds Weight measurement Control charts Noise measurement Estimation

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A Method of Discriminative Information Preservation and In-Dimension Distance Minimization Method for Feature Selection

A Method of Discriminative Information Preservation and In-D...

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international conference on Pattern Recognition

作者： Shangrong Huang Jian Zhang Xinwang Liu Lei Wang Advanced Analytics Institute University of Technology Sydney Sydney Australia Adv. Analytics Inst. University of Technology Sydney Sydney NSW AU School of Computer Science National University of Defence Technology Changsha Hunan China School of Computer Science and Software Engineering University of Wollongong NSW Australia

ISBN: (纸本)9781479952106

Preserving sample's pair wise similarity is essential for feature selection. In supervised learning, labels can be used as a direct measure to check whether two samples are similar with each other. In unsupervised learning, however, such similarity information is usually unavailable. In this paper, we propose a new feature selection method through spectral clustering based on discriminative information as an underlying data structure. Laplacian matrix is used to obtain more partitioning information than other previously proposed structures such as the Eigen space of original data. The high dimension of sample data is projected into a low dimensional space. The in-dimension distance is also considered to get a better compact clustering result. The proposed method can be solved efficiently by updating the projection matrix and its inverse normalized diagonal matrix. A comprehensive experimental study has demonstrated that the proposed method outperforms many state-of-the-art feature selection algorithms with different criterion including the accuracy of clustering/classification and Jaccard score.

关键词： Clustering algorithms Accuracy Feature extraction Laplace equations Face Support vector machines Educational institutions

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Principal Local Binary Patterns for Face Representation and Recognition

Principal Local Binary Patterns for Face Representation and ...

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international conference on Pattern Recognition

作者： Jun Yi Fei Su School of Information and Communication Engineering Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479952106

Based on fitting the Local Binary Patterns (LBP) histogram into the bag-of-words paradigm, we propose an LBP variant termed Principal Local Binary Patterns (PLBP) which are learned in an unsupervised way from the data. The learning problem turns out to be the same as the Principal Component Analysis (PCA) and thus can be solved very efficiently. Unlike the manually specified patterns in LBP which distribute very non-uniformly, the learned patterns in PLBP can adapt with the distribution of the data so that they distribute very uniformly, which preserves more information than LBP in the binary coding process. Moreover, PLBP can take advantage of much larger neighborhood than LBP to describe the point, which provides more information. Therefore, PLBP contains more information than LBP to discriminate different classes. The experimental results of face recognition on the FERET and LFW datasets clearly confirm the discrimination power and robustness of PLBP. It achieves very competing performance on both datasets and it is very simple and efficient to compute.

关键词： Encoding Vectors Face Histograms Transforms Covariance matrices Principal component analysis

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Multi-group Adaptation for Event Recognition from Videos

Multi-group Adaptation for Event Recognition from Videos

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international conference on Pattern Recognition

作者： Yang Feng Xinxiao Wu Han Wang Jing Liu Beijing Laboratory of Intelligent Information Technology Beijing Institute of Technology Beijing P.R. China

ISBN: (纸本)9781479952106

Recognizing events in consumer videos is becoming increasingly important because of the enormous growth of consumer videos in recent years. Current researches mainly focus on learning from numerous labeled videos, which is time consuming and labor expensive due to labeling the consumer videos. To alleviate the labeling process, we utilize a large number of loosely labeled Web videos (e.g., from YouTube) for visual event recognition in consumer videos. Web videos are noisy and diverse, so brute force transfer of Web videos to consumer videos may hurt the performance. To address such a negative transfer problem, we propose a novel Multi-Group Adaptation (MGA) framework to divide the training Web videos into several semantic groups and seek the optimal weight of each group. Each weight represents how relative the corresponding group is to the consumer domain. The final classifier for event recognition is learned using the weighted combination of classifiers learned from Web videos and enforced to be smooth on the consumer domain. Comprehensive experiments on three real-world consumer video datasets demonstrate the effectiveness of MGA for event recognition in consumer videos.

关键词： Videos YouTube Support vector machines Training Laplace equations Feature extraction Training data

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learning to Rank the Severity of Unrepaired Cleft Lip Nasal Deformity on 3D Mesh data

Learning to Rank the Severity of Unrepaired Cleft Lip Nasal ...

引用

international conference on Pattern Recognition

作者： Jia Wu Raymond Tse Linda G. Shapiro Department of Electrical Engineering University of Washington Seattle Washington U.S.A Seattle Children's Hospital Department of Surgery University of Washington Seattle WA U.S.A Department of Electrical Engineering Computer Science and Engineering University of Washington Seattle Washington U.S.A

Cleft lip is a birth defect that results in deformity of the upper lip and nose. Its severity is widely variable and the results of treatment are influenced by the initial deformity. Objective assessment of severity would help to guide prognosis and treatment. However, most assessments are subjective. The purpose of this study is to develop and test quantitative computer-based methods of measuring cleft lip severity. In this paper, a grid-patch based measurement of symmetry is introduced, with which a computer program learns to rank the severity of cleft lip on 3D meshes of human infant faces. Three computer-based methods to define the midfacial reference plane were compared to two manual methods. Four different symmetry features were calculated based upon these reference planes, and evaluated. The result shows that the rankings predicted by the proposed features were highly correlated with the ranking orders provided by experts that were used as the ground truth.

关键词： Three-dimensional displays Correlation Feature extraction Nose Manuals Mirrors Surgery

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A Unified Model for Human Behavior Modeling Using a Hierarchy with a Variable Number of States

A Unified Model for Human Behavior Modeling Using a Hierarch...

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international conference on Pattern Recognition

作者： Hande Alemdar T.L.M van Kasteren M.E. Niessen A. Merentitis Cem Ersoy Department of Computer Engineering Bogaziçi University Istanbul Turkey AGT Int. Darmstadt Germany AGT International Darmstadt Germany

ISBN: (纸本)9781479952106

Human behavior modeling enables many applications for smart cities, smart homes, mobile phones and other domains. We present a hierarchical hidden Markov model for human activity recognition that uses semi-supervised learning to automatically learn the model parameters using only labeled data of the top-layer of the hierarchy. This significantly reduces the annotation requirements for such a model and simplifies the design of such a model, since the inherent structure of the activity is automatically learned from data. The design consideration that remains is the number of states used for representing the actions that an activity consists of. Using multiple real world datasets we show that the same model works both for the recognition of activities of daily living in a smart home and for recognizing office activities from audio data. We show how a variable number of action states per activity can result in a significant increase in performance over using a fixed number per activity. Finally, we show how the use of Bayesian and Akaike information criterion results in models using a sub-optimal set of action states, since a model using intuitively chosen set states is able to outperform them.

关键词： Hidden Markov models data models Smart homes Complexity theory Mathematical model Bayes methods Context modeling

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Multi-view Based AdaBoost Classifier Ensemble for Class Prediction from Gene Expression Profiles

Multi-view Based AdaBoost Classifier Ensemble for Class Pred...

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international conference on Pattern Recognition

作者： Le Li Zhiwen Yu Jiming Liu Jane You Hau-San Wong Guoqiang Han School of Computer Science and Engineering South China University of Technology Department of Computer Science Hong Kong Baptist University Department of Computing Hong Kong Polytechnic University Department of Computer Science City University of Hong Kong

Multi-view learning, one of the important sub-fields in the area of machine learning, has gained more and more attention in class prediction of gene expression datasets. In this paper, we propose a new classifier ensemble framework, named as multi-view based Ad-a boost classifier ensemble framework (MV-ACE), which not only utilizes a random view generation technique to regulate different views and applies adaboost to adjust the training set, but also designs an adaptive process which explores the feasible combination of multiple views through an optimization process. Traditional multi-view learning focuses on exploring diverse views and the best integration of multiple views in a straight-forward manner, such as the linear combination of different views. Our proposed model, however, additionally applies a progressive training approach to improve the accuracies of the base classifiers. Moreover, we investigate the assembly of views at the model level, and employ an adaptive process to optimize the multi-view learning model to improve its performance. Our experiments on 12 cancer gene data sets for the classification task show that(i) MV-ACE works well on a diverse class of cancer gene expression profiles. (ii) It outperforms most of the state-of-the-art classifier ensemble approaches on these datasets.

关键词： Adaptation models Gene expression Training Accuracy Cancer Support vector machines Optimization

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