1. TUD MediaEval 2012 Tagging Task
Reporter: Martha A. Larson
Multimedia Information Retrieval Lab
Delft University of Technology
05-10-2012
Delft
University of
Technology
Challenge the future

2. Outline
• TUD-MM: Multi-modality video categorization with one-
vs-all classifiers
• Peng Xu, Yangyang Shi, Martha A. Larson
• MediaEval 2012 Tagging Task: Prediction based on One Best
List and Confusion Networks
• Yangyang Shi, Martha A. Larson, Catholijn M. Jonker
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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3. TUD-MM:Multi-modality video
categorization with one-vs-all
classifiers
Peng Xu, Yangyang Shi, Martha A. Larson
05-10-2012
Delft
University of
Technology
Challenge the future

4. Introduction
• Features from different modalities
• Visual feature
• Visual Words based representation & Global video representation
• Text features
• ASR, Metadata
• Term-frequency, LDA
• Classification and Fusion
• One-vs-all linear SVMs
• Reciprocal Rank Fusion
• Post-processing procedure to assign one category label for each video
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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5. Visual representations
• Visual words based video representation
• SIFT features are extracted from each key-frame
• Visual vocabulary is build by hierarchical k-means clustering
• The normalized term-frequency of the entire video
• Global video representation
• Edit features
• Content features
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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6. Classification and Fusion
• One-vs-all linear SVM
• C is determined by the 5-folder cross-validation
• Reciprocal Rank Fusion (RRF)*
• K=60 is to balance the importance of the lower ranked items
• The weights w(r) are determined by the cross-validation errors
from each modalities
• Post-processing procedure
* G. V. Cormack, C. L. A. Clarke, and S. Buettcher. Reciprocal rank fusion outperforms
Condorcet and individual rank learning methods. SIGIR '09, pages 758-759..
•
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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7. Result analysis
• MAP of different runs
Run_1 Run_2 Run_3 Run_4 Run_5 *Run_6 *Run_7
MAP 0.0061 0.3127 0.2279 0.3675 0.2157 0.0577 0.0047
• Run_1 to Run_5 are official runs
• Run_6 is the visual-only run without post-processing
• Run_7 is the visual-only run with global feature
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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8. Performance of visual features
Random basline VW Global
0,025
0,02
0,015
0,01
0,005
0
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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9. MediaEval 2012 Tagging Task:
Prediction based on One Best List and
Confusion Networks
Yangyang Shi, Martha A. Larson, Catholijn M. Jonker
05-10-2012
Delft
University of
Technology
Challenge the future

10. Models for One-best list and
Confusion Networks
Dynamic
Bayesian
Networks
Support Conditional
vector random
machine fields
ASR
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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11. One-best List SVM
Linear
Cut-off 3 kernel multi-
TF-IDF
vocabulary class SVM
(c=0.5)
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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12. One-best List DBN
E1 E2 E3
T1 T2 T3
W1 W2 W3
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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13. One-best List DBN
•
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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14. Results on Only ASR Run
Models MAP
Run2-one-best SVM 0.23
Run2-one-best DBN 0.25
Run2-one-best CRF 0.10
Run2-CN-CRF 0.09
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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15. Average Precision on Each Genre
0,8
0,7
0,6
0,5
0,4
DBN
0,3
0,2 SVM
0,1
0
TUD MediaEval 2012 Tagging Task
Visual similarity measures for semantic video retrieval
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16. Discussion and Future work
• Discussion
• Visual only methods can be improved in several ways
• Features selection or dimensional reduction methods can be applied.
• Genre-level video representation
• CRF failure
• A document is treated as a item rather than one word.
• Feature size is too big to converge.
• DBN outperforms SVM: The sequence order information probably helps
prediction
• Potentials
• Generate clear and useful labels
Visual similarity measures MediaEval 2012 Tagging Task
Video Search Reranking for Genre retrieval
TUD for semantic video Tagging
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17. Thank you!
Visual similarity measures for semantic Genre retrieval
Video Search Reranking for video Tagging
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