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Compressed sensing ensemble classifier for human detection

Compressed sensing ensemble classifier for human detection

引用

4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Zhang, Baochang Liu, Juan Gao, Yongsheng Liu, Jianzhuang Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering BeiHang University Beijing 100191 China School of Engineering Griffith University Australia Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9783642420566

This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of base classifiers. The major contributions of this paper are: 1) a novel principled framework for ensemble classifier design based on compressed sensing;2) a new concept of considering both the simplicity of ensemble classifier and irrelevance of base classifiers towards optimal classifier design;and 3) a quadratic function for CSEC optimization which includes a new optimizable positive semi-definite relevance matrix to simultaneously select appropriate base classifiers with minimized relevance. Experimental results on INRIA and SDL databases show that the performance of CSEC is better than two most popular classifiers SVM and AdaBoost, as well as a most recent method CLML. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Compressed sensing

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ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED SPARSE CODING FOR ABNORMALITY DETECTION

ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED SPARSE CODI...

引用

IEEE International Conference on Image Processing

作者： Sheng Han Ruiqing Fu Suzhen Wang Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The Chinese University of Hong Kong

ISBN: (纸本)9781479923427

This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The adaptability characteristic also helps reduce the dictionary's size to a small scale, since it only needs to keep recent or active information. We also introduce a simple, but effective, judgement criterion for abnormal detection based on sparse coding over weighted bases. Because of the condensed dictionary and the simplified judgment criterion, our algorithm performs online learning and online detection with a high speed and a high accuracy in various scenes.

关键词： Dictionary Learning Adaptive Learning Sparse Coding Abnormality Detection Sparse coding anomaly detection video data Dictionaries as Topic video monitoring

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LTD: Local Ternary Descriptor for Image Matching

LTD: Local Ternary Descriptor for Image Matching

引用

IEEE International Conference on Information and Automation

作者： Yongqiang Gao Yu Qiao Zhifeng Li Chunjing Xu Shenzhen Key Lab. of Comput. Vision &amp Pattern Recognition Shenzhen Inst. of Adv. Technol. Chinese Univ. of Hong Kong Shenzhen China|c|

ISBN: (纸本)9781479913329

Binary descriptors are receiving extensive research interests due to their storage and computation efficiency. A good binary descriptor should deliver sufficient information as well as be robust to image deformation and distortion. Recently, Calonder et al proposed Binary Robust Independent Elementary Features (BRIEF), which showed good performance in image matching. In this paper, we extend BRIEF to a Local Ternary Descriptor (LTD). Compared with BRIEF, LTD introduces a threshold to describe the difference of two pixels into three values. Our ternary descriptor can deliver more discriminative information than BRIEF while being robust to image deformation. We examine the key-point matching performance of LTD on several public datasets. The experimental results exhibit that LTD outperforms BRIEF.

关键词： local ternary descriptor descriptors binary pattern Hamming distance

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Online non-feedback image re-ranking via dominant data selection

Online non-feedback image re-ranking via dominant data selec...

引用

20th ACM International Conference on Multimedia, MM 2012

作者： Cao, Chen Chen, Shifeng Li, Yuhong Liu, Jianzhuang Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab. Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450310895

Image re-ranking aims at improving the precision of keyword-based image retrieval, mainly by introducing visual features to re-rank. Many existing approaches require offline training for every keyword, which are unsuitable for online image search. Other real-time approaches demand user interaction, which are inappropriate for large-scale image collection. To improve the accuracy of web image retrieval in a practicable manner, we propose a novel re-ranking algorithm to explore the cluster information of the image set. First, we build spectral graph on images that retrieved bysearch engine, and remove isolated nodes as noisy images. Then, we select positive samples from the most dominant cluster in initial top-ranked images, and the samples are used for semi-supervised learning and ranking. Our algorithm is online and non-feedback. Experiments on two public databases demonstrate that our algorithm outperforms the state-of-the-art approaches. © 2012 ACM.

关键词： Supervised learning

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Locating high-density clusters with noisy queries

Locating high-density clusters with noisy queries

引用

International Conference on pattern recognition

作者： Chen Cao Shifeng Chen Changqing Zou Jianzhuang Liu Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong China

ISBN: (纸本)9781467322164

Semi-supervised learning (SSL) relies on a few labeled samples to explore data's intrinsic structure through pairwise smooth transduction. The performance of SSL mainly depends on two folds: (1) the accuracy of labeled queries, (2) the integrity of manifolds in data distribution. Both of these qualities would be poor in real applications as data often consist of several irrelevant clusters and discrete noise. In this paper we propose a novel framework to simultaneously remove discrete noise and locate the high-density clusters. Experiments demonstrate that our algorithm is quite effective to solve several problems such as non-feedback image re-ranking and image co-segmentation.

关键词： Noise Noise measurement Clustering algorithms Databases Vectors Manifolds Semisupervised learning

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Clustering-based Discriminative Locality Alignment for Face Gender recognition

Clustering-based Discriminative Locality Alignment for Face ...

引用

IEEE/RSJ International Conference on Intelligent Robotics and Systems

作者： Duo Chen Jun Cheng Dacheng Tao College of Communication Engineering Chongqing University Chongqing 400044 China. He is also with the Shenzhen Key Laboratory of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China. He is also with the Chinese University of Hong Kong and Guangdong Provincial Key Laboratory of Robotics and Intelligent System. Center for Quantum Computation and Intelligent System Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia.

ISBN: (数字)9781467317368

ISBN: (纸本)9781467317375

To facilitate human-robot interactions, human gender information is very important. Motivated by the success of manifold learning for visual recognition, we present a novel clustering-based discriminative locality alignment (CDLA) algorithm to discover the low-dimensional intrinsic submanifold from the embedding high-dimensional ambient space for improving the face gender recognition performance. In particular, CDLA exploits the global geometry through k-means clustering, extracts the discriminative information through margin maximization and explores the local geometry through intra cluster sample concentration. These three properties uniquely characterize CDLA for face gender recognition. The experimental results obtained from the FERET data sets suggest the superiority of the proposed method in terms of recognition speed and accuracy by comparing with several representative methods.

关键词： FERET CDLA comparing recognition (Psychology) Gender Global geometry Compare Face Face

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Automatic motion-guided video stylization and personalization 11

Automatic motion-guided video stylization and personalizatio...

引用

19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Cao, Chen Chen, Shifeng Zhang, Wei Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

Video stylization transfers a source video into an artistic version while maintaining temporal coherence between adjacent frames. In this paper, we formulate the unsupervised example-based video stylization with Markov random field model. In our algorithm, we implement an improved optical flow algorithm to maintain temporal coherence while improve the accuracy of estimation along motion boundaries. We also extend our algorithm to the application of video personalization, in which human faces keep clear and distinguishable. A series of techniques are fused in video personalization, including face detection and alignment, motion flow, skin detection, and illumination blending. Given a source video and a style template image, our algorithm produces the stylized and/or personalized video(s) automatically. Experimental results demonstrate that our algorithm performs excellently in both video stylization and personalization. Copyright 2011 ACM.

关键词： Face recognition

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Edge-preserving single image super-resolution 11

Edge-preserving single image super-resolution

引用

19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Zhou, Qiang Chen, Shifeng Liu, Jianzhuang Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

This paper proposes a novel approach to single image super-resolution. First, an image up-sampling scheme is proposed which takes the advantages of both bilateral filtering and mean shift image segmentation. Then we use a shock filter to enhance strong edges in the initial up-sampling result and obtain an intermediate high-resolution image. Finally, we enforce a reconstruction constraint on the high-resolution image so that fine details can be inferred by back projection. Since strong edges in the intermediate result are enhanced, ringing artifacts can be suppressed in the back projection step. We compare our algorithm with several state-of-the-art image super-resolution algorithms. Qualitative and quantitative experimental results demonstrate that our approach performs the best. Copyright 2011 ACM.

关键词： Image segmentation

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Automatic object segmentation from large scale 3D urban point clouds through manifold embedded mode seeking 11

Automatic object segmentation from large scale 3D urban poin...

引用

19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Yu, Zhiding Xu, Chunjing Liu, Jianzhuang Au, Oscar C. Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

This paper presents a system that can automatically segment objects in large scale 3D point clouds obtained from urban ranging images. The system consists of three steps: The first one involves a ground detection process that can detect relatively complex terrain and separate it from other objects. The second step superpixelizes the remaining objects to speed up the segmentation process. In the final step, a manifold embedded mode seeking method is adopted to segment the point clouds. Even though the segmentation of urban objects is a challenging problem in terms of accuracy and problem scale, our system can efficiently generate very good segmentation results. The proposed manifold learning effectively improves the segmentation performance due to the fact that continuous artificial objects often have manifold-like structures. Copyright 2011 ACM.

关键词： Image segmentation

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3D object retrieval with semantic attributes 11

3D object retrieval with semantic attributes

引用

19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Gong, Boqing Liu, Jianzhuang Wang, Xiaogang Tang, Xiaoou Department of Information Engineering Chinese University of Hong Kong Hong Kong Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781450306164

Humans are capable of describing objects using attributes, such as "the object looks circular and is man-made". Motivated by these high-level descriptions, we build a user-friendly 3D object retrieval system, where the user can browse the database and search for targeted objects using semantic attributes. The main advantage of our system is that it does not require the user to find or sketch a 3D object as the query for 3D object retrieval. Besides, to the best of our knowledge, our system has obtained the best retrieval performance on three popular benchmarks. Copyright 2011 ACM.

关键词： Semantics

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