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Learning Flexible Binary Code for Linear Projection Based Hashing with Random Forest

Learning Flexible Binary Code for Linear Projection Based Ha...

引用

International Conference on pattern recognition

作者： Shuze Du Wei Zhang Shifeng Chen Yafei Wen Chengdu Institute of Computer Applications Chinese Academy of Sciences Sichuan China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Media Lab. Huawei Technologies Co. Ltd. China Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Guangdong China The Chinese University of Hong Kong Hong Kong University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479952106

Existing linear projection based hashing methods have witnessed many progresses in finding the approximate nearest neighbor(s) of a given query. They perform well when using a short code. But their code length depends on the original data dimension, thus their performance can not be further improved with higher number of bits for low dimensional data. In addition, in the case of high dimensional data, it is not a good choice to produce each bit by a sign function. In this paper, we propose a novel random forest based approach to cope with the above shortcomings. The bits are obtained by recording the paths when a point traversing each tree in the forest. Then we propose a new metric to calculate the similarity between any two codes. Experimental results on two large benchmark datasets show that our approach outperforms its counterparts and demonstrate its superiority over the existing state-of-the-art hashing methods for descriptor retrieval.

关键词： Vegetation Vectors Binary codes Encoding Computational modeling Radio frequency Principal component analysis

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A novel feature extracting method for dynamic gesture recognition based on support vector machine

A novel feature extracting method for dynamic gesture recogn...

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International Conference on Information and Automation (ICIA)

作者： Yuanrong Xu Qianqian Wang Xiao Bai Yen-Lun Chen Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Chinese Academy of Sciences University of Science and Technology of China Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences

In this paper, we propose a method based on the SVM algorithm to recognize dynamic hand gestures. The information of motion trajectory is captured by a leap motion in three-dimension space. A new methodology of feature extracting is proposed to guarantee the length of samples being the same. The elements of feature vectors are ranged according to two different criteria: one is the amplitude of the variation of orientation angles, and the other criterion is the order of the appearance of features. Experimental results show that this method can classify the dynamic hand gestures effectively.

关键词： Vectors Trajectory Feature extraction Equations Mathematical model Hidden Markov models Automation

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Dynamic hand gesture early recognition based on Hidden Semi-Markov Models

Dynamic hand gesture early recognition based on Hidden Semi-...

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IEEE International Conference on Robotics and Biomimetics

作者： Qianqian Wang Yuanrong Xu Yen-Lun Chen Yong Wang Xinyu Wu University of Science and Technology of China Shenzhen Key Lab for Computer Vision and Pattern Recognition Chinese Academy of Sciences Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong. Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences

ISBN: (纸本)9781479973989

Real-time performance can be greatly improved, if the early recognition is implemented. In this paper, a dynamic hand gesture early recognition system is proposed. The system can recognize the gesture before it is completed. Our method is based on the Hidden Semi-Markov Models. Three-dimensional information of the gesture trajectory collected by leapmotion is the main data we used. Experiments on the dataset which we established demonstrate the effectiveness of our method.

关键词： Hidden Markov models Gesture recognition Training Conferences Trajectory Arrays

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AgentSwitch: Towards smart energy tariff selection

AgentSwitch: Towards smart energy tariff selection

引用

12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

作者： Ramchurn, Sarvapali D. Osborne, Michael A. Parson, Oliver Rahwan, Talal Maleki, Sasan Reece, Steve Huynh, Trung D. Alam, Mudasser Fischer, Joel E. Rodden, Tom Moreau, Luc Roberts, Stephen Electronics and Computer Science University of Southampton United Kingdom Pattern Recognition Lab. University of Oxford United Kingdom Mixed Reality Lab. University of Nottingham United Kingdom

ISBN: (纸本)9781627487665

In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. Agent-Switch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalab.e collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the flow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Binary alloys

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AgentSwitch: Towards smart energy teriff selection

AgentSwitch: Towards smart energy teriff selection

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12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

作者： Ramchum, Sarvapali D. Osborne, Michael A. Parson, Oliver Rahwan, Talal Maleki, Sasan Reece, Steve Huynh, Trung D. Alam, Mudasser Fischer, Joel E. Rodden, Tom Moreau, Luc Roberts, Stephen Electronics and Computer Science University of Southampton United Kingdom Pattern Recognition Lab. University of Oxford United Kingdom Mixed Reality Lab. University of Nottingham United Kingdom

ISBN: (纸本)9781627487665

We present AgentSwitch, a prototype agent-based platform to solve the tariff selection problem for homeowners. AgentSwitch incorporates novel algorithms that work on the coarse data provided by smart meters to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. Our demo will allow users to interact with AgentSwitch and explore test user accounts in order to understand the impact of different usage profiles and appliance loads. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Smart power grids

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Dual training and dual prediction for polarity classification

Dual training and dual prediction for polarity classificatio...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Xia, Rui Wang, Tao Hu, Xuelei Li, Shoushan Zong, Chengqing Department of Computer Science Nanjing University of Science and Technology China NLP Lab. Department of Computer Science Soochow University China National Lab. of Pattern Recognition Institute of Automation CAS China

ISBN: (纸本)9781937284510

Bag-of-words (BOW) is now the most popular way to model text in machine learning based sentiment classification. However, the performance of such approach sometimes remains rather limited due to some fundamental deficiencies of the BOW model. In this paper, we focus on the polarity shift problem, and propose a novel approach, called dual training and dual prediction (DTDP), to address it. The basic idea of DTDP is to first generate artificial samples that are polarity-opposite to the original samples by polarity reversion, and then leverage both the original and opposite samples for (dual) training and (dual) prediction. Experimental results on four datasets demonstrate the effectiveness of the proposed approach for polarity classification. © 2013 Association for Computational Linguistics.

关键词： Forecasting

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On road vehicle make and model recognition via sparse feature coding

On road vehicle make and model recognition via sparse featur...

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Iranian Machine vision and Image Processing (MVIP)

作者： A. Nazemi M. J. Shafiee Z. Azimifar Computer Vision and Pattern Recognition Lab Shiraz University Shiraz Iran

Automatic vehicle Make and Model recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition system extracting the vehicle sub-image from the background and studies some sparse feature coding methods such as Orthogonal Matching Pursuit (OMP), some variation of Sparse Coding (SC) methods and compares them to choose the best one. Our method employs the sparse feature coding methods on dense Scale-Invariant Feature Transform (SIFT) features and Support Vector Machine (SVM) for classification. The proposed system is examined by an Iranian on road vehicles dataset, which its samples are in different point of views, various weather conditions and illuminations.

关键词： Feature extraction Vehicles Encoding Dictionaries Image coding Kernel computer vision

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A new approach to speed up in action recognition based on key-frame extraction

A new approach to speed up in action recognition based on ke...

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Iranian Machine vision and Image Processing (MVIP)

作者： Neda Azouji Zohreh Azimifar Computer Vision and Pattern Recognition Lab Shiraz University Shiraz Iran

Human action recognition is the process of lab.ling videos contain human motion with action classes. The run time complexity is one of the most important challenges in action recognition. In this paper, we address this problem using video abstraction techniques including key-frame extraction and video skimming. At first we extract key-frames and then skim the video clip by concatenating excerpts around the selected key-frames. This shorter sequence is used as input for classifier. Our proposed approach not only reduces the space complexity but also reduces the run time in both train and test steps. The experimental results provided on KTH action datasets show that the proposed method achieves good performance without losing considerable classification accuracy.

关键词： Videos Feature extraction Accuracy Image color analysis Shape Support vector machines computer vision

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Facial landmark localization based on hierarchical pose regression with cascaded random ferns 13

Facial landmark localization based on hierarchical pose regr...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Zhanpeng Zhang, Wei Liu, Jianzhuang Tang, Xiaoou Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450324045

The main challenge of facial landmark localization in realworld application is that the large changes of head pose and facial expressions cause substantial image appearance variations. To avoid high dimensional regression in the 3D and 2D facial pose spaces simultaneously, we propose a hierarchical pose regression approach, estimating the head rotation, facial components and landmarks hierarchically. The regression process works in a unified cascaded fern framework. We present generalized gradient boosted ferns (GBFs) for the regression framework, which give better performance than traditional ferns. The framework also achieves real time performance. We verify our method on the latest benchmark datasets. The results show that it outperforms state-of-theart methods in both accuracy and speed. Copyright © 2013 ACM.

关键词： Regression analysis

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Multi-feature canonical correlation analysis for face photo-sketch image retrieval 13

Multi-feature canonical correlation analysis for face photo-...

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21st ACM International Conference on Multimedia, MM 2013

作者： Gong, Dihong Li, Zhifeng Liu, Jianzhuang Qiao, Yu Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Media Lab Huawei Technologies Co. Ltd. China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450324045

Automatic face photo-sketch image retrieval has attracted great attention in recent years due to its important applications in real life. The major difficulty in automatic face photo-sketch image retrieval lies in the fact that there exists great discrepancy between the different image modalities (photo and sketch). In order to reduce such discrepancy and improve the performance of automatic face photo-sketch image retrieval, we propose a new framework called multi-feature canonical correlation analysis (MCCA) to effectively address this problem. The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithm using multiple features combined with two different random sampling methods in feature space and sample space. In this framework, we first represent each photo or sketch using a patch-based local feature representation scheme, in which histograms of oriented gradients (HOG) and multi-scale local binary pattern (MLBP) serve as the local descriptors. Canonical correlation analysis (CCA) is then performed on a collection of random subspaces to construct an ensemble of classifiers for photo-sketch image retrieval. Extensive experiments on two public-domain face photo-sketch datasets (CUFS and CUFSF) clearly show that the proposed approach obtains a substantial improvement over the state-of-the-art. Copyright © 2013 ACM.

关键词： Image retrieval

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