© Robert B. Yeganeh June 26, 2006 - ittc.ku.edu · June 26, 2006 © Robert B. Yeganeh A...
Transcript of © Robert B. Yeganeh June 26, 2006 - ittc.ku.edu · June 26, 2006 © Robert B. Yeganeh A...
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June 26, 2006© Robert B. Yeganeh
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June 26, 2006© Robert B. Yeganeh
A MultiA Multi--Level Approach for Level Approach for Temporal Video Temporal Video
Segmentation based on Segmentation based on Adaptive ExamplesAdaptive Examples
© Robert Babak Yeganeh
June 26, 2006
Committee
Dr. John Gauch (Chair)
Dr. Arvin Agah
Dr. James Miller
Submitted to the Department of Electrical Engineering and Computer Science and the Faculty of the Graduate School of the University of Kansas in partial fulfillment of the requirements for the degree of Master of Science in Computer Science.
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on Adaptive Examples(S.A.E.)
5. Experimentation Results
6. Conclusions
1. Overview
3. Segmentation based on Predefined Examples(S.P.E.)
2. Literature Review
A Multi-Level Approach for Video Temporal Segmentation based on Adaptive Examples
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on Adaptive Examples(S.A.E.)
5. Experimentation Results
6. Conclusions
1. Overview
3. Segmentation based on Predefined Examples(S.P.E.)
2. Literature Review
Outline
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June 26, 2006© Robert B. Yeganeh
1. Overview
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June 26, 2006© Robert B. Yeganeh
1. Overview
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June 26, 2006© Robert B. Yeganeh
1. Overview
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on Adaptive Examples(S.A.E.)
5. Experimentation Results
6. Conclusions
1. Overview
3. Segmentation based on Predefined Examples(S.P.E.)
2. Literature Review
Outline
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June 26, 2006© Robert B. Yeganeh
• Representation• Detection• Classification• False Detection and Prevention
2. Literature Review
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June 26, 2006© Robert B. Yeganeh
2. Literature Review
Fig.P.1. Illustrates a general process flow for temporal video segmentation algorithms.
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June 26, 2006© Robert B. Yeganeh
Problems & Solutions• (P) Quality of Detection
• (S) Parallel Analyzer• (S) Uncertainty Groups• (S) Extremely Sensitive Change Detector (ESCD)• (S) False Negative Prevention (No Threshold)• (S) False Positive Detection
• (P) Complexity vs. Simplicity• (S) Example based Technique• (S) Uncertainty Groups
• (P) Real Time• (S) Adaptive Examples
• (P) Generality vs. Specificity• (S) Example based Technique
• (P) Flexibility and Extensibility• (S) Multi-level property• (S) Example based Technique
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on Adaptive Examples(S.A.E.)
5. Experimentation Results
6. Conclusions
1. Overview
3. Segmentation based on Predefined Examples(S.P.E.)
2. Literature Review
Outline
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3. Segmentation based on Predefined Examples (S.P.E.)
3.1. Representation3.2. Detection3.3. Classification
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June 26, 2006© Robert B. Yeganeh
3.1. Representation• Predefined Examples
– Quality• Examples of different durations• Different types of examples
– Transition Types– Video Types
• Balance• Color Variety• Combined Transitions
– Quantity
Fig. P.2. Sample cut, fade in, fade out, and dissolve sequences.
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June 26, 2006© Robert B. Yeganeh
3.1. Representation
Table P.1. Organizes the twenty seven moments in an easy to understand fashion
• Color Moments (Statistics)
Fig. P.3. Illustrates the center of gravities for each of the three color components in real life picture.
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June 26, 2006© Robert B. Yeganeh
3.1. Representation
( ) ∑ ⋅=xy
x ctMxctyxI
NctM
),(),,,(1, (P.4)
( )[ ]∑ −⋅⋅
=xy
xx
x ctMxctyxIctMN
ctS 2),(),,,(),(
1),( (P.5)
( )[ ]33,(),,,(
),(1),( ∑ −⋅
⋅=
xyx
xx ctMxctyxI
ctMNctK (P.6)
(P.1)
(P.2)
(P.3)
∑=xy
ctyxIN
ctM ),,,(1),(
[ ]∑ −=xy
ctMctyxIN
ctS 2),(),,,(1),(
[ ]33),(),,,(1),( ∑ −=
xyctMctyxI
NctK
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June 26, 2006© Robert B. Yeganeh
3.1. Representation
F
I
Dd
F
I
Mm
Diff
p
F
f
I
i
p
ifif
p
F
f
I
i
p
ifif
′−′⋅
+
′−′⋅
=
∑∑
∑∑
=
=
=
=
/1
0
0,,
/1
0
0,,
βα
(P.7)
where
ifiifif
iifif
ifiifif
i
iifif
ifif
if
ifif
if
MofderivativewDD
wMM
mofderivativewdd
imomentforusedweightw
wmm
MofderivativeD
examplecurrentthefromframecurrentofimomentM
mofderivatived
streaminputthefromframecurrenttheofimomentm
,,,
,,
,,,
,,
,,
,
,,
,
′=⋅=′
⋅=′
′=⋅=′
=
⋅=′
=
=
=
=
• Measure of Difference
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June 26, 2006© Robert B. Yeganeh
3.1. Representation
Fig. P.4. Illustrates generated fit values image.
Fig. P.5. Illustrates sorted fit values image
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June 26, 2006© Robert B. Yeganeh
ifif Kmthreshold ,, σ⋅+= (P.8)
• Best Examples Extraction & Labeling
• Localized Adaptive Threshold
Fig. P.6. Represents the best fit values for each window for one minute of input data.
3.2. Detection3.3. Classification
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on Adaptive Examples(S.A.E.)
5. Experimentation Results
6. Conclusions
1. Overview
3. Segmentation based on Predefined Examples(S.P.E.)
2. Literature Review
Outline
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on AdaptiveExamples (S.A.E.)
4.1. Representation4.2. Detection4.3. Classification4.4. False Detection and Prevention
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June 26, 2006© Robert B. Yeganeh
4.1. Representation• Color Moments (Statistics)
– Refer to S.P.E. representation section.
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June 26, 2006© Robert B. Yeganeh
4.1. Representation• Adaptive Examples
– Cut– Dissolve– Fade– Normal Groups
Fig. P.8. Illustrates the process of extracting potential candidate and generating a dissolve adaptive example while T partition of the window is centered on a dissolve transitions.
Fig. P.7. Illustrates the process of extracting potential candidate and generating a cut adaptive example while T partition of the window is centered on a cut transition.
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June 26, 2006© Robert B. Yeganeh
4.1. Representation• Dissolve & Fade (Adaptive Examples)
(P.9)
(P.10)
RitRitRit BAM ,,,,,, )1( ⋅−+⋅= αα
2222,,
,,,,)1(
RitRit BARit σασασ ⋅−+⋅≈
Fig. P.9. Illustrates the process of extracting potential candidate and generating a fade in adaptive example while T partition of the window is centered on a fade in transition.
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June 26, 2006© Robert B. Yeganeh
4.1. Representation• Normal Groups (Adaptive Examples)
– No Threshold– Extremely Sensitive Change Detector
Fig. P.10. Illustrates the process of extracting potential candidate and generating a normal adaptive example for gradual transitions detector while T partition of the window is over a region of no activity (regions containing minor object motions).
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June 26, 2006© Robert B. Yeganeh
4.2. Detection4.3. Classification
• Best Examples Extraction & Labeling• No Threshold•
- Parallel Analyzer- Uncertainty Groups Analyzers- False Detection Techniques
4.4. False Prevention & Detection
Fig. P.11. Illustrates the high level process flow for the second algorithm.
Fig. P.12. Illustrates cut and dissolve detection streams.
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on Adaptive Examples(S.A.E.)
5. Experimentation Results
6. Conclusions
1. Overview
3. Segmentation based on Predefined Examples(S.P.E.)
2. Literature Review
Outline
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June 26, 2006© Robert B. Yeganeh
5. Experimentation Results5.1. Evaluation Techniques5.2. S.P.E. Results5.3. S.A.E. Results5.4. Discussion
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5.1. Evaluation Techniques• Manual Detection (Truth Data)• True Positives• False Positives• False Negatives• True Negatives• Recall• Precision• Utility
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June 26, 2006© Robert B. Yeganeh
5.1. Evaluation Techniques• Recall
%100Re ×+
== xmissed
xcorrect
xcorrectx
NNN
Rcall (P.11)
where
,Θ== xcorrectNTP { } { }{ }φ≠∩∈∃∈=Θ x
jxi
xm
xa
xi SSandkjkiS ,...,1|,...,1,
,Θ== xmissedNFN { } { }{ }φ=∩∈∀∈=Θ x
jxi
xm
xa
xi SSandkjkiS ,...,1|,...,1,
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June 26, 2006© Robert B. Yeganeh
5.1. Evaluation Techniques• Precision
%100Pr ×+
== xfalse
xcorrect
xcorrectx
NNN
Pecision (P.12)
where
,Θ== xfalseNFP { } { }{ }φ=∩∈∀∈=Θ x
jxi
xa
xm
xj SSandkikjS ,...,1|,...,1,
,Θ== xcorrectNTP { } { }{ }φ≠∩∈∃∈=Θ x
jxi
xm
xa
xi SSandkjkiS ,...,1|,...,1,
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June 26, 2006© Robert B. Yeganeh
5.1. Evaluation Techniques• Utility
– General Definition
– Variation Used
ecisioncallUtility Pr)1(Re ⋅−+⋅= αα (P.13)
2)Pr(Re ecisioncallUtility += (P.14)
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5.2. S.P.E. Results
Fig. P.13. Presents the experimentation results for 45 minutes of data.
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5.2. S.P.E. Results
Fig. P.14. Presents the experimentation results for 30 minutes of data.
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5.3. S.A.E. Results
Table P.2. Presents the final results of the second algorithm.
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5.3. S.A.E. Results
Table P.3. Presents number of true positives, false negatives, false positives, as well as recall, precision and utility for different thresholds used in false positive detector of cut detector.
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June 26, 2006© Robert B. Yeganeh
5.3. S.A.E. Results
Fig. P.15. Presents the recall and precision values for different thresholds used in false positive detector of cut detector as well as the ROC curve for the second algorithm.
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5.3. S.A.E. Results
Fig. P.16. Presents the utility values for different thresholds used in false positive detector of cut detector as well as the utility curve for the second algorithm.
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5.4. Discussion• S.P.E.
+ Simplicity+ Generality+ Flexibility+ Extensibility- Real Time- High Quality Detection
• S.A.E.+ Simplicity~ Generality+ Flexibility~ Extensibility+ Real Time+ High Quality Detection
Table P.4. Presents the time performance of the second algorithm for one minute of data.
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June 26, 2006© Robert B. Yeganeh
4. Segmentation based on Adaptive Examples(S.A.E.)
5. Experimentation Results
6. Conclusions
1. Overview
3. Segmentation based on Predefined Examples(S.P.E.)
2. Literature Review
Outline
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June 26, 2006© Robert B. Yeganeh
6. Conclusions6.1. Summary6.2. Future Works6.3. Acknowledgements6.4. References6.5. Q & A Session
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June 26, 2006© Robert B. Yeganeh
6.1. Summary• Two methods were implemented and tested
• The first one was based on lots of predefined examples• The second one was based on adaptive examples
• The latter method outperformed the first
• Our solutions directed all the problems of previous works:
• High Quality Detection• Simplicity• Real time• Generality• Flexibility• Extensibility
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June 26, 2006© Robert B. Yeganeh
6.2. Future Works• Future Enhancements
– Direct Comparison based on Predefined Examples– Direct Comparison based on Adaptive Examples
• Next Generation Algorithm– Simultaneous detections & use of specialized clustering
methods to increase generality
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June 26, 2006© Robert B. Yeganeh
Dr. John Gauch (Thesis Committee Chair)
Dr. Arvin Agah (Thesis Committee Member)
Dr. James Miller (Thesis Committee Member)
6.3. Acknowledgements
June 26, 2006
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6.4. References1) Yeganeh, Robert, “A Multi-Level Approach for Video Shot Boundary
Detection based on Adaptive Examples,” M.S. Thesis, KU, Lawrence, KS, 2006.
2) Refer to [1] for list of references used during our work.
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6.5. Q & A Session