Fcv hum mach_belongie

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Fcv hum mach_belongie

  1. 1. Human-in-the-Loop Fine-Grained Visual Categorization using Visipedia or "The Revolution will be Curated" Serge Belongie UC San Diego Steve Branson Catherine Wah Peter Welinder Boris Babenko Pietro Perona Florian Schroff
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  3. 3. Birds-200 Dataset6033 images over 200 bird species
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  5. 5. MTurker Label Certainty 5
  6. 6. Visual 20 Questions• “Computer Vision” module = Vedaldi’s VLFeat• VQ Geometric Blur, color/gray SIFT spatial pyramid• Multiple Kernel Learning• Per-Class 1-vs-All SVM• 15 training examples per bird species• Choose question to maximize expected Information Gain 6
  7. 7. 7
  8. 8. General Observations• User Responses are Stochastic• Computer Vision Reduces Manual Labor• User Responses Drive Up Performance• Computer Vision Improves Overall Performance• Different Questions are Asked w/ and w/o Computer Vision• Recognition is not Always Successful 8
  9. 9. w/o Computer Vision• User Responses are Stochastic 9
  10. 10. w/ Computer Vision• Computer Vision Reduces Manual Labor 10
  11. 11. w/ Computer Vision (cont’d)• User Responses Drive Up Performance 11
  12. 12. • Computer Vision Improves Overall Performance• Different Questions are Asked w/ and w/o Computer Vision
  13. 13. • Recognition is not Always Successful
  14. 14. Indigo Bunting Blue Grosbeak

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