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检索条件"机构=MOE-MS Key Laboratory of Multimedia Computing and Communication"

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Error resilient coding based on reversible data hiding and redundant slice

Error resilient coding based on reversible data hiding and r...

引用

International Conference on Image and Graphics

作者： Xu, Jiajia Zhang, Weiming Yu, Nenghai Zhu, Feng Chen, Biao MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei 230026 China

ISBN: (纸本)9780769545417

Compressed video streams are sensitive to errors and losses when transmitted over wireless error-prone channels. In this paper, we propose an Error Resilient (ER) scheme based on Reversible Data Hiding (RDH) and Redundant Slice (RDH-RS). Reversible data hiding is quite effective in unequal protection and can provide satisfactory protection to vital information, while redundant slice works well in protecting mass important data. Our scheme exploits both advantages. At the encoder side, we apply RS protection to odd frames, and embed vital data, the motion vectors (MV) of even frames, into the redundant slice of odd frames by bidirectional-RDH method, thus providing protection to even frames. If an MV of an odd frame cannot be correctly decoded at the decoder side, the redundant slice of odd frame will be utilized for error concealment. If the lost MV belong to an even frame's macroblock (MB), then the MV will be retrieved from the redundant slice of prior odd frame and help restoration. As our data hiding method is reversible, no extra visual quality degradation will happen, and the computational burden is quite low. Experimental results demonstrate that RDH-RS method can provide much better protection to fragile compressed video when compared with the previous arts. © 2011 IEEE.

关键词： Steganography

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Adaptive error resilient coding based on FMO in wireless video transmission

Adaptive error resilient coding based on FMO in wireless vid...

引用

3rd International Conference on multimedia Information Networking and Security, MINES 2011

作者： Zhu, Feng Zhang, Weiming Yu, Nenghai Xu, Jiajia Wu, Gang MOE-Microsoft Key Laboratory of Multimedia Computing Snd Communication University of Science and Technology of China Hefei 230027 China

ISBN: (纸本)9780769545592

H.264/AVC is the latest video coding standard from ITU-T and ISO/IEC. Not only the compression performance is enhanced but also the robustness of video bit streams to errors is better. FMO (Flexible Macro-block Ordering) is a new error resilient tool adopted by H.264/AVC and it has a good performance of error resilience. In this paper, a novel FMO algorithm is proposed, and an adaptive coding scheme based on this new FMO algorithm is introduced. Experiment results show that the proposed FMO algorithm could achieve almost the same PSNR as the FMO Type 1, but has better robustness in wireless transmission, and the adaptive coding scheme can still guarantee a high PSNR in the case of high packet loss rate. © 2011 IEEE.

关键词： Errors

来源：评论

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Semi-supervised Classification via Low Rank Graph

Semi-supervised Classification via Low Rank Graph

引用

International Conference on Image and Graphics (ICIG)

作者： Liansheng Zhuang Haoyuan Gao Jingjing Huang Nenghai Yu MOE-MS Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei China

Graph plays a very important role in graph based semi-supervised learning (SSL) methods. However, most current graph construction methods emphasize on local properties of the graph. In this paper, inspired by the advances of compressive sensing, we present a novel method to construct a so-called low-rank graph (LR-graph) for graph based SSL methods. Assuming that the graph is sparse and low rank, our proposed method uses both the local property and the global property of the graph, and thus is better at capturing the global structure of all data. Compared with current graphs, LR-graph is more informative and discriminative, and robust to outliers. Experiments on generic object recognition show that LR-graph achieves state-of-the-art performance for graph based SSL methods.

关键词： Training Robustness Object recognition Noise Optimization Dictionaries Visualization

来源：评论

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A New Graph Constructor for Semi-supervised Discriminant Analysis via Group Sparsity

A New Graph Constructor for Semi-supervised Discriminant Ana...

引用

International Conference on Image and Graphics (ICIG)

作者： Haoyuan Gao Liansheng Zhuang Nenghai Yu MOE-MS Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei China

Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly labeled data. This paper studies the Semi-supervised Discriminant Analysis (SDA) algorithm, which aims at dimensionality reduction utilizing both limited labeled data and abundant unlabeled data. Different from other relative work, we pay our attention to graph construction, which plays a key role in graph based SSL methods. Inspired by the advances of compressive sensing, we propose a novel graph construction method via group sparsity, which means to constrain the reconstruct data to be sparse for each sample, and constrain the representation in each class to be quite similar. Experimental results show that our method can significantly improve the performance of SDA, and outperform state-of-the-art methods.

关键词： Training Sparse matrices Manifolds Training data Databases Robustness Image reconstruction

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Dynamic background subtraction using spatial-color binary patterns

Dynamic background subtraction using spatial-color binary pa...

引用

International Conference on Image and Graphics

作者： Zhou, Wei Liu, Yu Zhang, Weiming Zhuang, Liansheng Yu, Nenghai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei China Department of Information Research Zhengzhou Information Science and Technology Institute Zhengzhou China

ISBN: (纸本)9780769545417

In this paper, an efficient approach for background modeling and subtraction is proposed. It's based on a novel spatial-color feature extraction operator named spatialcolor binary patterns(SCBP). As the name implies, features extracted by this operator include spatial texture and color information. In addition, a refine module is designed to refine the contour of moving objects. Using the proposed method, we improve the accuracy of subtracting the background and detecting moving objects in dynamic scenes. A data-driven model is used in our method. For each pixel, first, a histogram of SCBP is extracted from the circular region, and then a model consist of several histograms is built. For a new observed frame, each pixel is labeled either background or foreground according to the matching degree between its SCBP histogram and its model, then the label is refined and finally the model of this pixel is updated. The proposed approach is tested on challenging video sequences, which shows that the proposed method performs much better than several texturebased methods. © 2011 IEEE.

关键词： Pixels

来源：评论

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Tracking Based on SURF and Superpixel

Tracking Based on SURF and Superpixel

引用

International Conference on Image and Graphics (ICIG)

作者： Yu Liu Wei Zhou Huagang Yin Nenghai Yu MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei China

In this paper we present a novel algorithm for object tracking in video sequence based on SURF key-point and super pixel. SURF key-point is very effective for object matching between two images and we can use it to locate object by and large. But only in this way it can't guarantee the success of object tracking because many available key-points are un-matched and can't be used to locate the object. In order to solve this problem we take the advantage of super pixel to obtain more available key-points based on its property that all points in the same super pixel region belong to the same object. At last we construct a weight map for each frame to refine the location of target object. The experiments demonstrate that our work is robust for tracking, especially dealing with occlusions and object transformations.

关键词： Target tracking Lattices Robustness Face Legged locomotion Equations

来源：评论

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Error Resilient Coding Based on Reversible Data Hiding and Redundant Slice

Error Resilient Coding Based on Reversible Data Hiding and R...

引用

International Conference on Image and Graphics (ICIG)

作者： Jiajia Xu Weiming Zhang Nenghai Yu Feng Zhu Biao Chen MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei China

Compressed video streams are sensitive to errors and losses when transmitted over wireless error-prone channels. In this paper, we propose an Error Resilient (ER) scheme based on Reversible Data Hiding (RDH) and Redundant Slice (RDH-RS). Reversible data hiding is quite effective in unequal protection and can provide satisfactory protection to vital information, while redundant slice works well in protecting mass important data. Our scheme exploits both advantages. At the encoder side, we apply RS protection to odd frames, and embed vital data, the motion vectors (MV) of even frames, into the redundant slice of odd frames by bidirectional-RDH method, thus providing protection to even frames. If an MV of an odd frame cannot be correctly decoded at the decoder side, the redundant slice of odd frame will be utilized for error concealment. If the lost MV belong to an even frame's macro block (MB), then the MV will be retrieved from the redundant slice of prior odd frame and help restoration. As our data hiding method is reversible, no extra visual quality degradation will happen, and the computational burden is quite low. Experimental results demonstrate that RDH-RS method can provide much better protection to fragile compressed video when compared with the previous arts.

关键词： Decoding Streaming media PSNR Encoding Visualization Degradation Wireless communication

来源：评论

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ContextRank: Personalized Tourism Recommendation by Exploiting Context Information of Geotagged Web Photos

ContextRank: Personalized Tourism Recommendation by Exploiti...

引用

International Conference on Image and Graphics (ICIG)

作者： Kai Jiang Peng Wang Nenghai Yu MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei China

In this paper, we propose a method: ContextRank, which utilizes the vast quantity of geo tagged photos in photo sharing website to recommend travel locations. To enhance the personalized recommendation performance, our method exploits different context information of photos, such as textual tags, geotags, visual information, and user similarity. ContextRank first detects landmarks from photos' GPS locations, and estimates the popularity of each landmark. Within each landmark, representative photos and tags are extracted. Furthermore, ContextRank calculates the user similarity based on users' travel history. When a user's geotagged photos are given, the landmark popularity, representative photos and tags, and the user similarity are used to predict the user preference of a landmark from different aspects. Finally a learning to rank algorithm is introduced to combine different preference predictions to give the final recommendation. Experiments performed on a dataset collected from Panoramio show that the ContextRank can obtain a better result than the state-of-the-art method.

关键词： Visualization Context History Geology Collaboration Global Positioning System Kernel

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Adaptive Error Resilient Coding Based on FMO in Wireless Video Transmission

Adaptive Error Resilient Coding Based on FMO in Wireless Vid...

引用

International Conference on multimedia Information Networking and Security (MINES)

作者： Feng Zhu Weiming Zhang Nenghai Yu Jiajia Xu Gang Wu MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei China

关键词： Encoding Wireless communication PSNR Streaming media Resilience Video sequences Markov processes

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An Automatic Matching Algorithm Based on SIFT Descriptors for Remote Sensing Ship Image

An Automatic Matching Algorithm Based on SIFT Descriptors fo...

引用

International Conference on Image and Graphics (ICIG)

作者： Xin You Nenghai Yu MOE-Microsoft key laboratory of Multimedia Computing and Communication University of Science and Technology HeFei Anhui China

SIFT judge the true or false targets according to the density of matching feature points. But for different images, the numbers of matching feature points are different, the density is different. So SIFT can't judge the real targets automatically, requiring human-computer interaction. This paper proposes an algorithm based on SIFT realizing the automatic judgment of real targets. Firstly, do the image segmentation. Secondly, match the feature points in every area. At last, do frequency domain analysis for each matching feature vectors. The experimental results show that our algorithm solves the problem that SIFT judges the real target needing human-computer interaction. And also, our algorithm can determine the location and number of real targets.

关键词： Algorithm design and analysis Remote sensing Frequency domain analysis Image matching Marine vehicles Feature extraction Oceans

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