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LIG at MediaEval 2012 affect task: use of a generic method

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LIG at MediaEval 2012 affect task: use of a generic method

  1. 1. LIG Quaero consortium at MediaEval 2012Affect task: Violent Scenes Detection Task Nadia Derbas, Franck Thollard, Bahjat Safadi and Georges Quénot UJF-LIG 4 October 2012
  2. 2. Outline • Global system architecture • Descriptors with optimization • Classification • Hierarchical fusion • Conceptual feedback • Re-ranking • Submitted runs • Conclusion04/10/12 LIG - Nadia Derbas 2
  3. 3. The classical classification pipeline 0101 0101 Discourse of President Bill ClintonPresident Clinton is 0101basking in some goodnews Signal Semantics Semantic gap 04/10/12 LIG - Nadia Derbas 3
  4. 4. 04/10/12 Text Audio Image Descriptor extraction Descriptor transformation Classification Descriptors and classifier variants fusionLIG - Nadia Derbas Conceptual feedback Higher level hierarchical fusion Re-ranking (re-scoring) The LIG classification pipeline Classification score4
  5. 5. Descriptors and variants Descriptor extraction: ● color: 4 x 4 x 4 RGB histogram; ● texture: 8 orientations x 5 scales Gabor transform; ● points of interest: bags of SIFTs: Harris-Laplace and dense sampling, hard and fuzzy clustering, use of color opponent SIFTs (van de Sande); ● Audio: bag of MFCCs, MFCCs only and MFCCs plus their first and second derivatives. ● Motion Descriptor optimization: ● power normalization: x ← xα, α ~ 0.4: good for sparse descriptors; ● principal component analysis: dimensionality reduction and noise removal;04/10/12 LIG - Nadia Derbas 5
  6. 6. Use of multiple classifiers • Tow different classification methods: • KNN • MSVM • Use of multiple SVMs to address the unbalanced data problem • Improves over regular SVM on highly imbalanced datasets • MSVM is generally better than kNN but not always04/10/12 LIG - Nadia Derbas 6
  7. 7. Hierarchical fusion • Late fusion of descriptor and classifier variants: get the maximum from each descriptor type: • fuse spatial variants • then fuse other variants • finally fuse classification results from different classifiers • Further hierarchical late fusion: fuse across different descriptors with similar types: • all color together, all texture together ... • then all visual together, all audio together ... • finally everything together A linear combination of the scores is used with weight optimized on the MediaEval development set.04/10/12 LIG - Nadia Derbas 7
  8. 8. Conceptual feedback ● Idea: using the probability(-like) scores predicted on the 11 concepts for building a new descriptor ● 11 component vector ● Trained with classifiers as the signal-based descriptors Late fusion between the original scores and the scores computed from classification on these original scores yield a small improvement on the MAP@100.04/10/12 LIG - Nadia Derbas 8
  9. 9. Temporal re-ranking ● Fact: shot within a video are semantically related, especially if they are close within the same video ● Idea: update shot scores according to neighbors’ scores ● May be done globally (whole video) (Mérialdo 2009) or locally (window of a few shots) (Safadi 2010). ● Case of the full video: • Compute a global score for a whole video from the scores of all shots it contains (typically average or a variant) • Update the score of each shot using the global video shot (typically a linear combination or a variant)04/10/12 LIG - Nadia Derbas 9
  10. 10. Submitted runs ● LIG-1: 0.3138 ● Hierarchical fusion of all available descriptor/classifier combinations including the concept score feedback descriptor including temporal re- ranking ● LIG-2: 0.3122 ● Hierarchical fusion of all available descriptor/classifier combinations including temporal re-ranking ● LIG-3: 0.3138 ● Hierarchical fusion of all available descriptor/classifier combinations including the concept score feedback descriptor ● LIG-4: 0.3122 ● Hierarchical fusion of all available descriptor/classifier combinations04/10/12 LIG - Nadia Derbas 10
  11. 11. Submitted runs Metric MAP@100 MAP P@100 Best 0.6506 0.3183 0.4833 LIG-1 0.3138 0.1723 0.3167 LIG-2 0.3122 0.1731 0.3034 LIG-3 0.3138 0.1307 0.3166 LIG-4 0.3122 0.1259 0.3033 Median 0.3122 0.1249 0.260004/10/12 LIG - Nadia Derbas 11
  12. 12. Conclusion ● Temporal re-ranking always improve the result or has no significant effect ● Conceptual feedback improve the precision in the head of the returned list (MAP@100, P@100) ● Motion descriptors ● Audio was used (small contribution) but not ASR ● Improvements still possible04/10/12 LIG - Nadia Derbas 12
  13. 13. Thank you for your attention! Questions?04/10/12 LIG - Nadia Derbas 13

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