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Multi-feature subspace learning via sparse correlation fusion and embedding

Multi-feature subspace learning via sparse correlation fusio...

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14th Pacific-Rim Conference on multimedia, PCM 2013

作者： Zhang, Hong Zhang, Yanpeng College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China

ISBN: (纸本)9783319037301

Subspace learning is most traditional and important in multimedia analysis. Numerous researches have focused on how to introduce machine learning and statistical methods to multimedia subspace learning for semantic understanding and denoising, and have gained remarkable achievement in different multimedia applications, such as content-based retrieval, data clustering, face recognition, etc. However, most of these researches are based on multimedia data of single modality. Nowadays, with the rapid development of multimedia and information technology, multimedia data of different modalities often coexist, and the presence of one has a complementary effect on the other to some extent. Because different multimedia data are usually represented with heterogeneous low-level features and there exists the wellknown semantic gap, it is interesting and challenging to learn multimedia semantics by multi-feature subspace learning of different modalities. In this paper, we analyze sparse canonical correlation between feature matrices of different multimedia data, construct an isomorphic sparse multi-feature subspace;moreover, we propose subspace optimization strategy with correlation fusion, which explores both geometrical-based content correlation and graph-based semantic correlation. Our algorithm has been applied to content-based multimodal retrieval and data classification. Comprehensive experiments have demonstrated the superiority of our method over several existing algorithms. © Springer International Publishing Switzerland 2013.

关键词： Face recognition

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On Opportunistic Coverage for Urban Sensing

On Opportunistic Coverage for Urban Sensing

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Dong Zhao Huadong Ma Liang Liu Jing Zhao Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications China Department of Computer Science Illinois Institute of Technology Chicago IL USA

Opportunistic sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to collect data ubiquitously for large-scale urban sensing. Existing work lacks an in-depth investigation on the sensing quality of such sensing systems, which faces two basic problems: 1) how to measure the sensing quality? and 2) how many humans or vehicles are necessary to satisfy the sensing quality requirement of the whole urban area? To solve the first problem, we propose a metric called Inter-Cover Time (ICT) to characterize the opportunity with which a sub region is covered, which reflects the sensing quality directly. According to the empirical measurement studies on real mobility traces of thousands of taxis collected in beijing and Shanghai, we find that the aggregated ICT Distribution (ICTD) closely resembles a truncated power-law distribution regardless of the size of sub regions and the number of vehicles. We also analyze the reasons behind this particular pattern by the evaluation on four known mobility models. To solve the second problem, we further propose a metric called opportunistic coverage ratio based on the ICTD to characterize the relationship between the sensing quality of an urban area and vehicle number. Our results provide fundamental guidelines on the measurement of sensing quality and network planning for opportunistic urban sensing applications.

关键词： Vehicles Sensors Global Positioning System Urban areas Wireless sensor networks Exponential distribution Mobile communication

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A two-stage object tracking method based on Curvelet transform and mean shift algorithm

A two-stage object tracking method based on Curvelet transfo...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Pengcheng Han Junping Du Qingping Li Ming Fang Yuehua Yang Yingmin Jia Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China Beihang University Beijing China

ISBN: (纸本)9781467351942

Traditional mean shift tracking algorithm couldn't track moving objects in cross-scale domain. In this paper, we propose a new two-stage object tracking method combined Curvelet Transform and mean shift algorithm. Our proposed method extracts image features using Curvelet transform, and calculates object location by cross-scale mean shift algorithm. The experimental results demonstrate that the proposed algorithm can effectively track moving objects. Compared with traditional mean shift algorithm, tracking accuracy has been significantly improved.

关键词： Transforms Object tracking Feature extraction Frequency-domain analysis Automobiles Satellites Covariance matrices

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Online web-video topic detection and tracking with semi-supervised learning

Online web-video topic detection and tracking with semi-supe...

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14th Pacific-Rim Conference on multimedia, PCM 2013

作者： Li, Guorong Zhang, Weigang Pang, Junbiao Huang, Qingming Jiang, Shuqiang Graduate University Beijing China Key Lab of Intell. Info. Process. CAS Inst. of Comput. Tech. Beijing China School of Computer Science and Technology Harbin Inst. of Tech. China Beijing Municipal Key Lab. of Multimedia and Intelligent Software Tec. Beijing Univ. of Tech China

ISBN: (纸本)9783319037301

With the rapid growth of web data, a large amount of web videos are available online. However, how to organize them for facilitating users' experience and government supervision remains a problem yet to be seriously investigated. Topic detection and tracking, which has been a hot research topic for decades, could cluster web videos into different topics according to their semantic content. However, how to online discover topic and track them from web videos and images has not been fully discussed. In this paper, we formulate topic detection and tracking as an online tracking, detection and learning problem. First, by learning from historical data including labeled data and plenty of unlabeled data using semi-supervised multi-class multi-feature method, we obtain a topic tracker which could also discover novel topics from the new stream data. Second, when new data arrives, an online updating method is developed to make topic tracker adapt to the evolution of the stream data. We conduct experiments on public dataset to evaluate the performance of the proposed method and the results demonstrate its effectiveness for topic detection and tracking. © Springer International Publishing Switzerland 2013.

关键词： Semantics

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UAV path planning for passive multi-target following in complex environment

UAV path planning for passive multi-target following in comp...

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Chinese Control Conference (CCC)

作者： Yueqian Liang Jingnan Wu Yingmin Jia Junping Du The Seventh Research Division Beihang University Beijing CN Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing Beijing CN

In this paper the problem of following multiple noncooperative and high agile surface targets using multiple fixed-wing unmanned aerial vehicles (UAVs) in complex environment is studied and a real-time rule-based target following algorithm is proposed. The complex environment consists of hard threats, soft threats and sensor missed detections. The multi-dimensional assignment algorithm is applied to address the data association problem, and the target trajectory approximation algorithm is then developed to determine the motion trajectories of the targets. And a simulation example is given to verify the efficiency of the proposed algorithm.

关键词： Trajectory Current measurement Approximation algorithms Approximation methods Vectors Turning

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COUPON: Cooperatively Building Sensing Maps in Mobile Opportunistic Networks

COUPON: Cooperatively Building Sensing Maps in Mobile Opport...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Dong Zhao Huadong Ma Shaojie Tang Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications China Department of Computer and Information Sciences Temple University USA

With the popularity and advancements of smartphones, mobile users can sense the city using variety of sensors opportunistically, and forward the sensory data to the monitoring center for building sensing maps through intermittent connections with short-range communications. In order to build sensing maps satisfying specific sensing quality with low delay and energy consumption, we design COUPON, a novel cooperative sensing and data forwarding framework. We first notice that cooperative sensing scheme can eliminate sampling redundancy and hence save energy. Then we design two cooperative forwarding schemes by leveraging data fusion: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF). Different from previous work assuming that all packets are propagated individually, we consider that packets are spatial-temporal correlated in the forwarding process, and derive the dissemination law of correlated packets. We theoretically prove that our cooperative forwarding schemes can achieve better tradeoff between delivery delay and transmission overhead. We evaluate our proposed framework and schemes with real mobile traces. Extensive simulations demonstrate that the cooperative sensing scheme can reduce the number of samplings by 93% compared with the non-cooperative scheme, ERF can reduce the transmission overhead by 78% compared with Epidemic Routing (ER), BSWF can increase the delivery ratio by 16%, and reduce the delivery delay and transmission overhead by 5% and 32% respectively, compared with Binary Spray-and-Wait (BSW).

关键词： Sensors Data integration Delays Erbium Mobile communication Monitoring Routing

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Saliency Preserved Image Fusion Using Nonsubsampled Contourlet Transform

Saliency Preserved Image Fusion Using Nonsubsampled Contourl...

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2013年中国智能自动化学术会议

作者： Liang Xu Junping Du Qingping Li JangMyung Lee Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Department of Electronics Engineering Pusan National University

The visual attention model inspired by the early primate visual system is a very important tool in image *** on the visual attention model, the paper proposes a novel saliency preserved image fusion algorithm with a nonsubsampled contourlet transform(NSCT).The basic idea is that the visual saliency map is first built on the coefficients of the NSCT using the visual attention model,and then is combined with the coefficients of the NSCT to form the activity level which is employed to select the final fused *** algorithm can transform successfully the visual sensitive information from source images into the fused image which contains abundant detailed contents and preserves effectively the saliency structure while enhances the image *** demonstrate that the proposed algorithm yields the encouraging results.

关键词： Saliency map Nonsubsampled contourlet transform Image fusion

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Adaptive Region Clustering in LDA Framework for Image Segmentation

Adaptive Region Clustering in LDA Framework for Image Segmen...

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2013年中国智能自动化学术会议

作者： Xiaoru Wang Junping Du Shuzhe Wu Fu Li Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Department of Electrical and Computer Engineering Portland State UniversityPortlandORUSA

Image segmentation based on low-level features has been studied for many ***,because of the semantic gap issue,it is difficult to have more breakthroughs based on low-level *** is a powerful tool to model co-occurrence relationships between words and thus is used to catch the semantic connections between low-level visual *** image segmentation,the codebook is built from the visual features and topics are trained with LDA *** the topic distributions yield important guidance for ***,in previous papers,researchers used the topic with the highest probability to merge the *** ignored the statistics nature of the topic ***,the segmentation result will be greatly impact by the codebook size,the topic number and cluster *** address these challenges,this paper proposes a new image segmentation algorithm based on LDA framework:an adaptive region clustering approach based on *** build the cookbook from the color,texture and SIFT features and perform the LDA training using Gibbs Sampling for *** the adaptive region clustering with EM is invented to merge the regions based on topic *** clustering number is self-identified according to Minimum Description Length(MDL) *** an image is represented as a Gaussian Mixture Model(GMM) with objects corresponding to Gaussian mixture components. The final segmentation could be achieved after the region clustering and adjacent *** implemented the new algorithm and conducted experiments to validate the region clustering approach and segmentation *** the results show great effectiveness of this new algorithm.

关键词： Topic model LDA Region clustering Adaptive clustering Image segmentation

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Undo the codebook bias by linear transformation for visual applications 13

Undo the codebook bias by linear transformation for visual a...

引用

21st ACM International Conference on multimedia, MM 2013

作者： Zhang, Chunjie Zhang, Yifan Wang, Shuhui Pang, Junbiao Liang, Chao Huang, Qingming Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Computer Science and Technology Beijing University of Technology 100124 Beijing China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China Department of Computer Sciences University of Texas at San Antonio San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

The bag of visual words model (BoW) and its variants have demonstrate their effectiveness for visual applications and have been widely used by researchers. The BoW model first extracts local features and generates the corresponding codebook, the elements of a codebook are viewed as visual words. The local features within each image are then encoded to get the final histogram representation. However, the codebook is dataset dependent and has to be generated for each image dataset. This costs a lot of computational time and weakens the generalization power of the BoW model. To solve these problems, in this paper, we propose to undo the dataset bias by codebook linear transformation. To represent every points within the local feature space using Euclidean distance, the number of bases should be no less than the space dimensions. Hence, each codebook can be viewed as a linear transformation of these bases. In this way, we can transform the pre-learned codebooks for a new dataset. However, not all of the visual words are equally important for the new dataset, it would be more effective if we can make some selection using sparsity constraints and choose the most discriminative visual words for transformation. We propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters. Image classification experimental results on several image datasets show the effectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Linear transformations

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Spatio-temporal super-resolution reconstruction based on robust optical flow and Zernike moment for dynamic image sequences

Spatio-temporal super-resolution reconstruction based on rob...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Meiyu Liang Junping Du Xiaoyong Li Liang Xu Honggang Liu Yawen Li Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781467351942

A novel and efficient spatio-temporal super-resolution reconstruction model based on robust optical flow and Zernike moment is proposed in this paper. The model does not rely on accurate estimation of subpixel motion, and is robust to noise and rotation. Moreover, it can effectively overcome the problems of block and hole artifacts in the traditional reconstruction methods. In our super-resolution process, first we propose an efficient robust optical flow motion estimation model based on the motion details protection, then introduce the bi-weighted fusion strategy to implement the spatio-temporal motion compensation, and finally apply the multi-frame information fusion scheme to make the spatio-temporal super-resolution reconstruction and optimization based on Zernike moment and non-local self-similarity. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of both subjective visual and objective quantitative evaluations.

关键词： Image motion analysis computer vision Optical imaging Spatial resolution Image reconstruction Integrated optics

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