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WhittleSearch : Image Search with Relative Attribute Feedback

WhittleSearch : Image Search with Relative Attribute Feedback. CVPR 2012 Adriana Kovashka Devi Parikh Kristen Grauman University of Texas at Austin Toyota Technological Institute Chicago (TTIC). Approach. Dataset At each iteration, the top K < N ranked images.

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WhittleSearch : Image Search with Relative Attribute Feedback

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  1. WhittleSearch: Image Search with Relative Attribute Feedback CVPR 2012 Adriana Kovashka Devi Parikh Kristen Grauman University of Texas at Austin Toyota Technological Institute Chicago (TTIC)

  2. Approach • Dataset • At each iteration, the top K < N ranked images

  3. Binary Relevance Feedback

  4. Binary Relevance Feedback • Current reference set • Score function

  5. Relative Attribute Feedback

  6. Learning Relative Attributes • Mechanical Turk (MTurk)

  7. Updating the scoring function from feedback • Feedback form • “ What I want is more/less/similarlym than image Itr”

  8. Three cases

  9. Score function of Relative Attribute Feedback • Take the intersection of all F feedback

  10. Hybrid Feedback Approach

  11. x denotes the Cartesian product

  12. Experimental Results

  13. Datasets • Shoes • 14,658 shoe images from like.com • attributes—‘pointy-at-the-front’, ‘open’, ‘bright-in-color’, ‘covered-with-ornaments’, ‘shiny’, ‘highat-the-heel’, ‘long-on-the-leg’, ‘formal’, ‘sporty’, and ‘feminine’ • PubFig • the Public Figures dataset of human faces • 772 images from 8 people and 11 attributes • OSR • the Outdoor Scene Recognition dataset of natural scenes • 2,688 images from 8 categories and 6 attributes

  14. Datasets • Training set • 750 triplets of images (i , j , k) from each dataset • Score correlation • Normalized Discounted Cumulative Gain at top K (NDCG@K) • K = 50

  15. Iteration experiments on the three datasets

  16. Amount of feedback

  17. Ranking accuracy with first feedback • Attribute meaning • “amount of perspective” on a scene is less intuitive than “shininess” on shoes

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