Innovation Together

Microsoft Research Asia Academic Research Collaboration

By: Lolan Song (Editor)

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Hardcover | 19 September 2008

At a Glance

Hardcover
240 Pages

Dimensions(cm)
23.39 x 15.6 x 1.42

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Since its establishment in 1998, Microsoft Research Asia's trademark and long term commitment has been to foster innovative research and advanced education in the Asia-Pacific region. Through open collaboration and partnership with universities, government and other academic partners, MSRA has been consistently advancing the state-of-the-art in computer science. This book was compiled to record these outstanding collaborations, as Microsoft Research Asia celebrates its 10th Anniversary.

The selected papers are all authored or co-authored by faculty members or students through collaboration with MSRA lab researchers, or with the financial support of MSRA. Papers previously published in top-tier international conference proceedings and journals are compiled here into one accessible volume of outstanding research. Innovation Together highlights the outstanding work of Microsoft Research Asia as it celebrates ten years of achievement and looks forward to the next decade of success.

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Published: 16th November 2014

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Next Generation Multimedia
Drag-and-Drop Pasting	p. 3
Introduction	p. 4
Related work	p. 5
Optimal Boundary	p. 6
Poisson image editing	p. 6
Boundary energy minimization	p. 7
A shortest closed-path algorithm	p. 8
Fractional boundary	p. 10
A blended guidance field	p. 10
Experimental Results	p. 12
Conclusion and Discussion	p. 14
Motion Patches: Building Blocks for Virtual Environments Annotated with Motion Data	p. 17
Introduction	p. 17
Background	p. 18
Data Acquisition and Processing	p. 19
Motion Patch Construction	p. 22
Stitching Motion Patches	p. 24
Tilable Patches	p. 25
Animation and Control	p. 26
Experimental Results	p. 27
Discussion	p. 29
Video Completion by Motion Field Transfer	p. 35
Introduction	p. 35
Related Work	p. 36
Overview of Our Approach	p. 37
Video Completion Algorithm	p. 37
Local motion estimation	p. 38
Dissimilarity measure of motion vectors	p. 38
Motion Field Transfer	p. 38
Color Propagation	p. 39
Experimental Results	p. 40
Results of Video Completion	p. 40
Another Application: Frame Interpolation	p. 43
Discussion	p. 44
Conclusion	p. 45
Correlative Multi-Label Video Annotation	p. 47
Introduction	p. 47
First Paradigm: Individual Concept Annotation	p. 48
Second Paradigm: Context Based Conceptual Fusion Annotation	p. 49
Third Paradigm: Integrated Multi-label Annotation	p. 49
Our Approach-CML	p. 50
A Multi-Label Classification Model	p. 50
Learning the Classifier	p. 52
Connection with Gibbs Random Fields for Multi-Label Representation	p. 53
Implementation Issues	p. 55
Interacting concepts	p. 55
Concept Label Vector Prediction	p. 57
Concept Scoring	p. 58
Experiments	p. 59
Data Set Description	p. 59
Experiment Setup	p. 60
Experiment Results	p. 61
Conclusions and Future Works	p. 62
References	p. 63
Stereoscopic Video Synthesis from a Monocular Video	p. 65
Introduction	p. 65
Related Works	p. 66
Overview	p. 67
The Cost Function	p. 69
Stereo Cost	p. 70
Similarity Cost	p. 72
Continuity Cost	p. 72
Optimization	p. 73
Initialization	p. 74
Speed-up	p. 74
Optimization for Visual Smoothness	p. 76
Results and Discussions	p. 76
Conclusions	p. 78
References	p. 80
ShapePalettes: Interactive Normal Transfer via Sketching	p. 83
Motivation	p. 84
Contributions and Related Work	p. 85
Interacting with Shape Palettes	p. 86
Shape Palettes in Use	p. 88
Discussion and Limitations	p. 88
Summary	p. 89
References	p. 91
Adaptive Directional Lifting-based Wavelet Transform for Image Coding	p. 93
Introduction	p. 94
2D Wavelet Transform via Adaptive Directional Lifting	p. 96
ADL structure	p. 96
Subpixel Interpolation	p. 99
R-D Optimized Segmentation for ADL	p. 101
Experimental Results and observations	p. 102
Conclusions and future work	p. 107
References	p. 108
Networking and Systems
Low-Power Distributed Event Detection in Wireless Sensor Networks	p. 113
Introduction	p. 113
Related Work	p. 114
Paper Organization	p. 115
Analysis of Detection and Lifetime	p. 115
System Model	p. 115
Detection Analysis	p. 116
Tradeoff Characterization	p. 117
CAS: Coordinated Wakeup Scheduling	p. 118
Overview	p. 118
Distributed Scheduling Coordination	p. 119
Aggressive Wakeup Adjustment	p. 120
Performance Evaluation	p. 120
Experiment Setting	p. 120
Results	p. 120
Conclusion	p. 123
Understanding User Behavior in Large-Scale Video-on-Demand Systems	p. 125
Introduction	p. 125
The PowerInfo VOD System	p. 126
User Access Patterns	p. 128
User Accesses over time	p. 129
User Arrival Rates	p. 130
Session Lengths	p. 132
Implications	p. 135
Popularity and User Interest	p. 135
Pareto Principle	p. 135
Request (Popularity) Distribution	p. 136
Rate of Change in User interest	p. 139
Understanding Popularity	p. 141
Introduction of New Content	p. 141
Impact of Recommendations	p. 142
Related Work	p. 145
Conclusion	p. 146
References	p. 147
A representation theorem for minmax regret policies	p. 149
Introduction	p. 149
Three qualitative decision criteria	p. 150
When does a policy have minmax regret representation?	p. 151
Conclusions	p. 154
References	p. 154
A Note on the Cramer-Damgard Identification Scheme	p. 155
Introduction	p. 155
Description of the Cramer-Damgard Intended-Verifier ID Scheme	p. 156
[Sigma]-protocol and [Sigma subscript OR]-protocol	p. 156
Description of protocol	p. 157
Two Man-in-the-Middle Attacks	p. 157
The replaying attack	p. 157
The interleaving attack	p. 158
Concluding Remarks	p. 158
References	p. 159
Natural User Interface
A Tree-based Kernel Selection Approach to Efficient Gaussian Mixture Model - Universal Background model based Speaker Identification	p. 163
Introduction	p. 163
GMM-UBM identification system	p. 165
Tree-based kernel selection	p. 167
Distortion measure & cluster centroid	p. 167
Tree construction	p. 168
Mixture components selection	p. 168
Tree structure & algorithm efficiency	p. 169
Observation reordering based pruning	p. 170
Beam search	p. 170
Observation reordering based pruning (ORBP)	p. 171
Experiments	p. 171
Experimental setup	p. 171
Experimental results	p. 172
Conclusions	p. 175
Word Graph Based Feature Enhancement for Noisy Speech Recognition	p. 177
Introduction	p. 177
Word Graph Based Feature Enhancement	p. 178
System Overview	p. 178
Signal Processing Based Speech Enhancement	p. 179
First Pass Decoding and Word Graph Construction	p. 179
Model Based Clean Speech Synthesis	p. 180
Wiener Filtering and Constrained Second Pass Decoding	p. 181
Experiments	p. 182
Experimental Setup	p. 182
Signal Processing Based Speech Enhancement	p. 182
GMM Based Feature Enhancement	p. 183
Word Graph Based Feature Enhancement	p. 183
Conclusions	p. 184
References	p. 184
Search and Mining
Building Bridges for Web Query Classification	p. 189
Introduction	p. 189
Problem Definition	p. 191
Query and Category Enrichment	p. 191
Enrichment through Search Engines	p. 192
Word Matching Between Categories	p. 192
Classification Approaches	p. 192
Classification by Exact Matching	p. 193
Classification by SVM	p. 193
Our New Method: Classifiers by Bridges	p. 194
Experiments	p. 197
Data Set and Evaluation Metrics	p. 197
Results and Analysis	p. 198
Related Work	p. 202
Conclusion and Future Work	p. 202
References	p. 203
Spectral Domain-Transfer Learning	p. 205
Introduction	p. 205
Preliminaries on Spectral Methods	p. 207
Cross-Domain Spectral Classification	p. 207
Problem Definition	p. 207
Objective Function	p. 207
Incorporating Constraints	p. 209
Optimization	p. 210
Case Study	p. 212
Experiments	p. 212
Data Sets	p. 213
Evaluation Metric	p. 215
Comparison Methods	p. 215
Implementation Details	p. 215
Experimental Results	p. 216
Related Work	p. 218
Spectral Methods	p. 218
Transfer Learning	p. 219
Conclusion and Future Work	p. 220
References	p. 220
Table of Contents provided by Ingram. All Rights Reserved.

Innovation Together

Microsoft Research Asia Academic Research Collaboration

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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