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E-raamat: Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video

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(Mitsubishi Electric Research Labs, Cambridge, MA, USA), (Professor, Univer), (Mitsubishi Electric Research Labs, Cambridge, MA, USA), (United Technologies Research Center, East Hartford, CT, USA), (Microsoft Research, Redmond, WA, USA)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Jan-2006
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780080481531
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Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance VideoSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Jan-2006
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780080481531
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Large volumes of video content can only be easily accessed by the use of rapid browsing and retrieval techniques. Constructing a video table of contents (ToC) and video highlights to enable end users to sift through all this data and find what they want, when they want are essential. This reference puts forth a unified framework to integrate these functions supporting efficient browsing and retrieval of video content. The authors have developed a cohesive way to create a video table of contents, video highlights, and video indices that serve to streamline the use of applications in consumer and surveillance video applications.

The authors discuss the generation of table of contents, extraction of highlights, different techniques for audio and video marker recognition, and indexing with low-level features such as color, texture, and shape. Current applications including this summarization and browsing technology are also reviewed. Applications such as event detection in elevator surveillance, highlight extraction from sports video, and image and video database management are considered within the proposed framework. This book presents the latest in research and readers will find their search for knowledge completely satisfied by the breadth of the information covered in this volume.

* Offers the latest in cutting edge research and applications in surveillance and consumer video

* Presentation of a novel unified framework aimed at successfully sifting through the abundance of footage gathered daily at shopping malls, airports, and other commercial facilities

* Concisely written by leading contributors in the signal processing industry with step-by-step instruction in building video ToC and indices

Large volumes of video content can only be easily accessed by the use of rapid browsing and retrieval techniques. Constructing a video table of contents (ToC) and video highlights to enable end users to sift through all this data and find what they want, when they want are essential. This reference puts forth a unified framework to integrate these functions supporting efficient browsing and retrieval of video content. The authors have developed a cohesive way to create a video table of contents, video highlights, and video indices that serve to streamline the use of applications in consumer and surveillance video applications.

The authors discuss the generation of table of contents, extraction of highlights, different techniques for audio and video marker recognition, and indexing with low-level features such as color, texture, and shape. Current applications including this summarization and browsing technology are also reviewed. Applications such as event detection in elevator surveillance, highlight extraction from sports video, and image and video database management are considered within the proposed framework. This book presents the latest in research and readers will find their search for knowledge completely satisfied by the breadth of the information covered in this volume.

* Offers the latest in cutting edge research and applications in surveillance and consumer video

* Presentation of a novel unified framework aimed at successfully sifting through the abundance of footage gathered daily at shopping malls, airports, and other commercial facilities

* Concisely written by leading contributors in the signal processing industry with step-by-step instruction in building video ToC and indices

Muu info

This book COMPLETELY covers the techniques of video summarization, browsing and retrieval.
List of Figures
xi
List of Tables
xvii
Preface xix
Acknowledgments xxi
Introduction
1(14)
Introduction
1(2)
Terminology
3(3)
Video Analysis
6(1)
Shot Boundary Detection
6(1)
Key Frame Extraction
6(1)
Play/Break Segmentation
7(1)
Audio Marker Detection
7(1)
Video Marker Detection
7(1)
Video Representation
7(4)
Video Representation for Scripted Content
8(1)
Video Representation for Unscripted Content
9(2)
Video Browsing and Retrieval
11(1)
Video Browsing Using ToC-Based Summary
11(1)
Video Browsing Using Highlights-Based Summary
11(1)
Video Retrieval
12(1)
The Rest of the Book
12(3)
Video Table-of-Content Generation
15(24)
Introduction
15(2)
Related Work
17(3)
Shot- and Key Frame-Based Video ToC
17(1)
Group-Based Video ToC
18(1)
Scene-Based Video ToC
19(1)
The Proposed Approach
20(10)
Shot Boundary Detection and Key Frame Extraction
20(1)
Spatiotemporal Feature Extraction
20(1)
Time-Adaptive Grouping
21(3)
Scene Structure Construction
24(6)
Determination of the Parameters
30(3)
Gaussian Normalization
30(1)
Determining WC and WA
31(1)
Determining groupThreshold and sceneThreshold
32(1)
Experimental Results
33(4)
Conclusions
37(2)
Highlights Extraction from Unscripted Video
39(58)
Introduction
39(3)
Audio Marker Recognition
39(1)
Visual Marker Detection
39(2)
Audio-Visual Marker Association and Finer-Resolution Highlights
41(1)
Audio Marker Recognition
42(10)
Estimating the Number of Mixtures in GMMs
42(2)
Evaluation Using the Precision-Recall Curve
44(2)
Performance Comparison
46(1)
Experimental Results on Golf Highlights Generation
47(5)
Visual Marker Detection
52(19)
Motivation
52(1)
Choice of Visual Markers
52(8)
Robust Real-Time Object Detection Algorithm
60(2)
Results of Baseball Catcher Detection
62(2)
Results of Soccer Goalpost Detection
64(4)
Results of Golfer Detection
68(3)
Finer-Resolution Highlights Extraction
71(25)
Audio-Visual Marker Association
71(1)
Finer-Resolution Highlights Classification
71(1)
Method 1: Clustering
72(1)
Method 2: Color/Motion Modeling Using HMMs
73(9)
Method 3: Audio-Visual Modeling Using CHMMs
82(3)
Experimental Results with DCHMM
85(11)
Conclusions
96(1)
Video Structure Discovery Using Unsupervised Learning
97(102)
Motivation and Related Work
97(1)
Proposed Inlinear/Outlier-Based Representation for ``Unscripted'' Multimedia Using Audio Analysis
98(3)
Feature Extraction and the Audio Classification Framework
101(10)
Feature Extraction
102(1)
Mel Frequency Cepstral Coefficients (MFCC)
102(1)
Modified Discrete Cosine Transform (MDCT) Features from AC-3 Stream
103(6)
Audio Classification Framework
109(2)
Proposed Time Series Analysis Framework
111(30)
Problem Formulation
112(1)
Kernel/Affinity Matrix Computation
113(1)
Segmentation Using Eigenvector Analysis of Affinity Matrices
114(3)
Past Work on Detecting ``Surprising'' Patterns from Time Series
117(2)
Proposed Outlier Subsequence Detection in Times Series
119(2)
Generative Model for Synthetic Time Series
121(1)
Performance of the Normalized Cut for Case 2
122(5)
Comparison with Other Clustering Approaches for Case 2
127(8)
Performance of Normalized Cut for Case 3
135(6)
Ranking Outliers for Summarization
141(13)
Kernel Density Estimation
141(1)
Confidence Measure for Outliers with Binomial and Multinomial PDF Models for the Contexts
142(7)
Confidence Measure for Outliers with GMM and HMM Models for the Contexts
149(4)
Using Confidence Measures to Rank Outliers
153(1)
Application to Consumer Video Browsing
154(25)
Highlights Extraction from Sports Video
154(17)
Scene Segmentation for Situation Comedy Videos
171(8)
Systematic Acquisition of Key Audio Classes
179(13)
Application to Sports Highlights Extraction
179(6)
Event Detection in Elevator Surveillance Audio
185(7)
Possibilities for Future Research
192(7)
Video Indexing
199(22)
Introduction
199(1)
Motivation
199(1)
Overview of MPEG-7
199(1)
Indexing with Low-Level Features: Motion
200(12)
Introduction
200(1)
Overview of MPEG-7 Motion Descriptors
201(1)
Camera Motion Descriptor
201(2)
Motion Trajectory
203(1)
Parametric Motion
203(1)
Motion Activity
204(2)
Applications of Motion Descriptors
206(2)
Video Browsing System Based on Motion Activity
208(4)
Conclusion
212(1)
Indexing with Low-Level Features: Color
212(1)
Indexing with Low-Level Features: Texture
213(1)
Indexing with Low-Level Features: Shape
214(1)
Indexing with Low-Level Features: Audio
215(2)
Indexing with User Feedback
217(1)
Indexing Using Concepts
218(1)
Discussion and Conclusions
219(2)
A Unified Framework for Video Summarization, Browsing, and Retrieval
221(16)
Video Browsing
221(2)
Video Highlights Extraction
223(4)
Audio Marker Detection
223(1)
Visual Marker Detection
224(1)
Audio-Visual Markers Association for Highlights Candidates Generation
225(1)
Finer-Resolution Highlights Recognition and Verification
226(1)
Video Retrieval
227(2)
A Unified Framework for Summarization, Browsing, and Retrieval
229(6)
Conclusions and Promising Research Directions
235(2)
Applications
237(12)
Introduction
238(1)
Consumer Video Applications
238(4)
Challenges for Consumer Video Browsing Applications
241(1)
Image/Video Database Management
242(2)
Surveillance
244(3)
Challenges of Current Applications
247(1)
Conclusions
247(2)
Conclusions
249(4)
Bibliography 253(8)
About the Authors 261(4)
Index 265
Thomas S. Huang received his B.S. Degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, China; and his M.S. and Sc.D. Degrees in Electrical Engineering from the Massachusetts Institute of Technology, Cambridge, Massachusetts. He was on the Faculty of the Department of Electrical Engineering at MIT from 1963 to 1973; and on the Faculty of the School of Electrical Engineering and Director of its Laboratory for Information and Signal Processing at Purdue University from 1973 to 1980. Dr. Huang's professional interests lie in the broad area of information technology, especially the transmission and processing of multidimensional signals. He has published 21 books, and over 600 papers in Network Theory, Digital Filtering, Image Processing, and Computer Vision. Among his many honors and awards: Honda Lifetime Achievement Award, IEEE Jack Kilby Signal Processing Medal, and the King-Sun Fu Prize of the International Association for Pattern Recognition.
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