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Leveraging Category-Level Labels For Instance-Level Image Retrieval

Leveraging Category-Level Labels For Instance-Level Image Retrieval. Outline. Introduction Learning techniques Experiment Conclusion. Outline. Introduction Learning techniques Experiment Conclusion. Introduction. The problem query-by-example instance level

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Leveraging Category-Level Labels For Instance-Level Image Retrieval

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  1. Leveraging Category-Level Labels For Instance-Level Image Retrieval

  2. Outline • Introduction • Learning techniques • Experiment • Conclusion

  3. Outline • Introduction • Learning techniques • Experiment • Conclusion

  4. Introduction • The problem • query-by-example instance level • The state-of-the-art method • SIFT [IJCV 2004] • BOV [ICCV 2003] • GIST [IJCV 2001] • Fisher vector [CVPR 2007] • VLAD [CVPR 2010]

  5. Introduction • The question • the source of labeled data • Can category-level labels be used to improve instance-level image retrieval?

  6. Introduction • The goal • Learn a better subspace in a supervised manner • The learning techniques • Metric learning framework • Attribute representations • Canonical Correlation Analysis (CCA) • Joint Subspace and Classifier Learning(JSCL)

  7. Introduction

  8. Introduction

  9. Introduction • The main contribution • category-level labeled data can be leveraged to improve instance-level retrieval • JSCL and a dimensionality reduction achieves this goal

  10. Outline • Introduction • Learning techniques • Experiment • Conclusion

  11. Metric Learning

  12. Metric Learning

  13. Attribute • Attribute-based representations • By training SVM classifier • The dimensionality of the subspace is fixed • Two approaches • PCA • Fisher vectors [CVPR 2011]

  14. Canonical Correlation Analysis • Project the multiple views into a common subspace where the correlation is maximal • Solve the singularity problem • in the cross-covariance matrices of canonical correlation analysis • CCA can be understood as an embedding of images and labels in a common subspce.

  15. Canonical Correlation Analysis

  16. Joint Subspace and Classifier Learning

  17. Outline • Introduction • Learning techniques • Experiment • Conclusion

  18. Experiment • Datasets

  19. Metric Learning • Results on Holidays (mAP,in %) • Results on UKB (4×recall@4)

  20. Attribute • Results on Holidays (mAP,in %), UKB (4×recall@4) • Results on Holidays (mAP,in%) after PCA

  21. CCA & JSCL • Results on Holidays (mAP,in%) • Results on UKB (4×recall@4)

  22. Experiment

  23. Experiment

  24. Outline • Introduction • Learning techniques • Experiment • Conclusion

  25. Conclusion • The first to show the usefulness of JSCL in this context • Metric learning and attributes do not improve significantly • Showed that CCA and JSCL,whichboth consist in embedding labels and images in a common subspace • Easily perform query-by-example and query-by-text searches

  26. Thank you

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