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KEYFRAME-BASED VIDEO
SUMMARIZATION DESIGNER
Carlos Ramos Caballero
Advisors: Horst Eidenberger and Xavier Giró I Nieto
Contents
 Introduction
 State of the art
 Methodology
 Results assessment
 Conclusions
2
 The application: Designer Master
DEMONSTRATION
3
Contents
 Introduction
 State of the art
 Methodology
 Results assessment
 Conclusions
4
Introduction
 Motivation
 Designer Master: keyframe-based video summarization interface
 Object Maps: system for automatic video summarization
5
Graphical User Interface
(Designer Master)
Computer Vision Engine
(Object Maps)
Introduction
 Goals of the thesis
6
Introduction
 Goals of the thesis
 Improving the keyframe extraction module
7
Introduction
 Goals of the thesis
 Improving the keyframe extraction module
 Assessing the improvement
8
Contents
 Introduction
 State of the art
 Methodology
 Results assessment
 Conclusions
9
State of the art
 Shot segmentation
10
Hierarchical decomposition and representation of video content [1]
[1] http://www.scholarpedia.org/article/Video_Content_Structuring
State of the art
 Shot segmentation example
11
Shot boundary detection example [2].
[2] Martos, M. “Content-based Video Summarization to Object Maps”, Vienna University of Technology, Austria (2013).
State of the art
 Shot segmentation techniques
 Pixel-to-pixel methods
• Global pixel-to-pixel
• Cumulative pixel-to-pixel
 Histogram-based methods
• Simple histogram
• Maximum histogram
• Weighted histogram
 Hausdorff method
12
Contents
 Introduction
 State of the art
 Methodology
 Results assessment
 Conclusions
13
Methodology: Implemented solution
 System architecture overview
14
Methodology : Implemented solution
 Uniform sampling
𝑓𝑝𝑠𝑖: frame rate of the input video.
𝐿𝑖: total number of frames of the input video.
𝑁0: total number of frames we want to keep (𝑁0=100).
15
Methodology : Implemented solution
 Gray scale domain
16
Color model transformation RGB to YIQ.
Methodology : Implemented solution
 Difference computation
Where 𝐼(𝑡,𝑖,𝑗) represents the intensity value at frame t in pixel(𝑖,𝑗).
X and Y are the width and height of the video frames, respectively.
17
Methodology : Implemented solution
 Normalization
Where 𝑑̂ is the normalized value, 256 is the number of grey levels, X and Y are the
width and height of the video frames, respectively.
18
Methodology : Implemented solution
 Decision making
The threshold value used in our application is 𝜏 = 0.1 (as defined in [2]).
19
[2] Martos, M. “Content-based Video Summarization to Object Maps”, Vienna University of Technology, Austria (2013).
Methodology: Environment
 Environment
20
Contents
 Introduction
 State of the art
 Methodology
 Results assessment
 Conclusions
21
Results assessment
 TEST 1: Testing the applications + ‘in situ’ survey
 11 participants
 Test data: The intouchables trailer
22
Results assessment
 Example: pair of summaries
23
Designer Master v1 Designer Master v2
Results assessment
 TEST 2: web-based survey
 43 participants
 Test data: The Intouchables trailer
24
Results assessment
 EVALUATION
 Quality of the generated summaries
 Representativeness of the generated summaries
 Mean Opinion Score
• 1. Unacceptable
• 2. Poor
• 3. Good
• 4. Very good
• 5. Excellent
25
Results assessment
 Quality generated summaries
“Please, rate summary 1”
26
“Please, rate summary 2”
Results assessment
 Quality generated summaries
27
MOS MOS – scores distribution
Results assessment
 Representativeness of the summaries
“Which summary let you better recognize the video content?”
28
Results assessment
 Representativeness of the summaries
29
Results assessment
 Ease-of-use of the application
“Do you think the application is intuitive and easy to use?”
30
Results assessment
 Ease-of-use of the application
31
Results assessment
 Execution time
32
Contents
 Introduction
 State of the art
 Methodology
 Results assessment
 Conclusions
33
Conclusions
 Accomplishment of the initial goals
 Improving the keyframe extraction module by integrating both
projects.
 Assessing the improvement.
34
Conclusions
 Accomplishment of the initial goals
 Improving the keyframe extraction module by integrating both
projects.
 Assessing the improvement.
 Our work has slightly improved Designer Master
 Users can create better video summaries and easily due the better
quality of the extracted keyframes.
35
Conclusions
 Accomplishment of the initial goals
 Improving the keyframe extraction module by integrating both
projects.
 Assessing the improvement.
 Our work has slightly improved Designer Master
 Users can create better video summaries and easily due the better
quality of the extracted keyframes.
 It is hoped to develop this work into a product for the Austrian
Broadcasting station ORF
36
Conclusions
 Accomplishment of the initial goals
 Improving the keyframe extraction module by integrating both
projects.
 Assessing the improvement.
 Our work has slightly improved Designer Master
 Users can create better video summaries and easily due the better
quality of the extracted keyframes.
 It is hoped to develop this work into a product for the Austrian
Broadcasting station ORF
37
Thank you very much for your attention!
Danke schön!
Moltes gràcies!
38

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