Evaluation Of Color Descriptors For Object And Scene


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Evaluation Of Color Descriptors For Object And Scene

  1. 1. Evaluation of Color Descriptors for Object and Scene Recognition<br />Authors: Koen E. A. van de Sande, Theo Gevers, and Cees G. M. Snoek @ University of Amsterdam<br />Presenter: Shao-Chuan Wang<br />
  2. 2. Evaluation of Color Descriptors for Object and Scene Recognition<br />Focus: Color features/descriptors on obj. and scene recognition<br />Summary: The invariance of photometric transform aces and its effect on discriminative power.<br />Conclusion: The usefulness of invariance is category-specific!<br />
  3. 3. Photometric transforms (1/2)<br />Light intensity scale invariant<br />Light intensity shift invariant<br />
  4. 4. Photometric transforms (1/2)<br />Light intensity scale and shift invariant<br />Light color change<br />Light color change and shift<br />
  5. 5. Color Descriptors (1/1)<br />Histograms <br />RGB, Hue, Saturation, rgHistogram, …<br />Color Moments: contain spatial info.<br />Color SIFT: combined color and SIFT<br />HSV-SIFT, Hue-SIFT, …<br />
  6. 6. Color Histograms (1/2)<br />RGB-histogram<br />Hue-histogram<br />H and S are scale-invariant and shift-invariant w.r.t light intensity<br />rg-histogram<br />Scale-invariant<br />Not shift-invariant<br />Not that b is redudant<br />Image from wikipedia<br />
  7. 7. Color Histograms (2/2)<br />Transformed color<br />Scale and shift-invariant w.r.t light intensity.<br />Opponent color histogram<br />O1,O2 shift invariant<br />O3: intensity, no invariant<br />
  8. 8. Color SIFT Descriptors (1/3)<br />HSV-SIFT<br />SIFT over HSV channels<br />Hue is unstable in gray axis<br />Hue-SIFT (Van de Weijer 2006)<br />Used Hue histogram weighing <br /> by its saturation<br />Concatenate Hue histogram with SIFT<br />Only SIFT is invariant; Hue histogram is not! (partial invariance)<br />Hue Instability<br />
  9. 9. Color SIFT Descriptors (2/3)<br />OpponentSIFT<br />SIFT over all channels in the opponent color space.<br />Shift-invariant to light intensity.<br />W-SIFT<br />Eliminate O1 and O2’s intensity information<br />Scale-invariant to light intensity<br />rg-SIFT<br />SIFT over r,g spaces<br />Scale and shift invariant, but not invariant to light color changes/shifts<br />
  10. 10. Color SIFT Descriptors (3/3)<br />Transformed color SIFT<br />SIFT over normalized transformed channels.<br />Scale- and shift-invariant to light color changes and shift.<br />
  11. 11. Experiments<br />Implementation:<br />Scale-invariants points are detected by Harris-Laplace point detectors<br />Color descriptors are computed over the area around the points; all regions are proportionally re-sampled to a uniform 60x60 patch.<br />Cluster descriptors with k = 40 (images) k = 4000 (video)<br />SVM classifier with EMD/chi-square kernel<br />
  12. 12. Benchmark (1/3)<br />Image: PASCAL VOC 2007, over 20 object categories<br />
  13. 13. Benchmark (2/3)<br />Most objs were categorized better under scale- and shift- invariant to light intensity<br />Some, such as car and dining table, do not benefit from such invariance.<br />
  14. 14. Benchmark (3/3)<br />Video: Mediamill Challenge, 39 object and scene categories<br />
  15. 15. Evaluation of Color Descriptors for Object and Scene Recognition<br />Conclusion:<br />W-SIFT and rgSIFT, in general, outperform other color descriptors.<br />Light intensity info. Is important for some categories<br />Usefulness of invariance is category-specific.<br />
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