1<br />Book Cover Recognition<br />SHAO-CHUAN (SHAWN) WANG<br />CMOS E6737 Biometrics Final Project<br />sw2644@columbia.e...
Book Cover Recognition<br />System Goal:<br />Input: A middle resolution image taken from web camera or cell phone (640 x ...
Baseline Model: Bag-of-words<br />Low level feature extraction (Dense SIFT) (CVPR09)<br />Visual words learning (a.k.a cod...
A Glance on Dataset (1/2)<br />9 books. training on half (15), test on half (15):<br />4<br />
A Glance on Dataset (2/2)<br />5<br />
Baseline: Experiments<br />System parameters:<br />15 training images per book, test on the rest.<br />Resize image size t...
More Challenging Testing Images<br />More clutter, more occlusion, more realistic.<br />7<br />
More Challenging: Experiments<br />Results:<br />Poor: 36.24% (MAP) <br />8<br />Confusion matrix<br />So, Can we “detect”...
Hough Transform<br />Assumptions:<br />Books are rectangular, some contrasts between books and clutter.<br />No many “fals...
Auto cropping results (1/2)<br />10<br />Easier cases:<br />
Auto cropping results (2/2)<br />Really difficult cases:<br />11<br />
More Challenging: Experiments<br />Results:<br />Without cropping: 36.24% (MAP)<br />With autocropping: 58.60% (MAP)<br />...
Book Cover Recognition
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Book Cover Recognition

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  • Book Cover Recognition

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

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