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Leveraging Category-Level Labels For Instance-Level Image Retrieval

This paper investigates the potential of leveraging category-level labeled data to improve instance-level image retrieval. It addresses the challenges inherent in query-by-example methods and reviews state-of-the-art techniques like SIFT, BOV, GIST, Fisher vectors, and VLAD. We propose a novel learning framework that includes Metric Learning, Canonical Correlation Analysis (CCA), and Joint Subspace and Classifier Learning (JSCL). Our experimental results on benchmark datasets demonstrate significant improvements in retrieval performance, establishing the effectiveness of category-level information in enhancing instance-level retrieval tasks.

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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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