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Book Cover Recognition

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Shao-Chuan Wang
Shao-Chuan Wang
Education
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1 Book Cover Recognition SHAO-CHUAN (SHAWN) WANG CMOS E6737 Biometrics Final Project sw2644@columbia.edu http://shaochuan.info/
Book Cover Recognition System Goal: Input: A middle resolution image taken from web camera or cell phone (640 x 480) that contains a book. Output: The book title/id. 2 Book Cover Recognition System “Learning from Data”
Baseline Model: Bag-of-words Low level feature extraction (Dense SIFT) (CVPR09) Visual words learning (a.k.a codebook learning) via vector quantization. Spatial pooling of local features. Linear SVM classification. (libLinear package) 3
A Glance on Dataset (1/2) 9 books. training on half (15), test on half (15): 4
A Glance on Dataset (2/2) 5
Baseline: Experiments System parameters: 15 training images per book, test on the rest. Resize image size to 300 pixels (long side) Visual codebook size (K = 225) # of spatial pyramid level = 0,1,2 Spatial pooling function = max L2-regularized 1-norm loss linear SVM 5-fold cross validation Results: Nearly perfect recognition results. (99.259%=134/135) 6
More Challenging Testing Images More clutter, more occlusion, more realistic. 7
More Challenging: Experiments Results: Poor: 36.24% (MAP) 8 Confusion matrix So, Can we “detect” and “crop” books automatically?
Hough Transform Assumptions: Books are rectangular, some contrasts between books and clutter. No many “false” strong edges on clutter. 9
Auto cropping results (1/2) 10 Easier cases:
Auto cropping results (2/2) Really difficult cases: 11
More Challenging: Experiments Results: Without cropping: 36.24% (MAP) With autocropping: 58.60% (MAP) 12
Book Cover Recognition

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Book Cover Recognition

  • 1. 1 Book Cover Recognition SHAO-CHUAN (SHAWN) WANG CMOS E6737 Biometrics Final Project sw2644@columbia.edu http://shaochuan.info/
  • 2. Book Cover Recognition System Goal: Input: A middle resolution image taken from web camera or cell phone (640 x 480) that contains a book. Output: The book title/id. 2 Book Cover Recognition System “Learning from Data”
  • 3. Baseline Model: Bag-of-words Low level feature extraction (Dense SIFT) (CVPR09) Visual words learning (a.k.a codebook learning) via vector quantization. Spatial pooling of local features. Linear SVM classification. (libLinear package) 3
  • 4. A Glance on Dataset (1/2) 9 books. training on half (15), test on half (15): 4
  • 5. A Glance on Dataset (2/2) 5
  • 6. Baseline: Experiments System parameters: 15 training images per book, test on the rest. Resize image size to 300 pixels (long side) Visual codebook size (K = 225) # of spatial pyramid level = 0,1,2 Spatial pooling function = max L2-regularized 1-norm loss linear SVM 5-fold cross validation Results: Nearly perfect recognition results. (99.259%=134/135) 6
  • 7. More Challenging Testing Images More clutter, more occlusion, more realistic. 7
  • 8. More Challenging: Experiments Results: Poor: 36.24% (MAP) 8 Confusion matrix So, Can we “detect” and “crop” books automatically?
  • 9. Hough Transform Assumptions: Books are rectangular, some contrasts between books and clutter. No many “false” strong edges on clutter. 9
  • 10. Auto cropping results (1/2) 10 Easier cases:
  • 11. Auto cropping results (2/2) Really difficult cases: 11
  • 12. More Challenging: Experiments Results: Without cropping: 36.24% (MAP) With autocropping: 58.60% (MAP) 12

Editor's Notes

  1. Left to the title, a presenter can insert his/her own image pertinent to the presentation.
  2. The End of Slideshow.