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Learning Collections of Part Models for Object Recognition

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A release version for the code is available in the following link. Some changes have been made to the classifier code to improve stability, so the performance will be similar, but will not exactly match the numbers reported in the paper.
[source]

Results

In the paper, the shape rescoring features erroneously included scores from color and texture classifiers in addition to the region shape classifier. The detection results for Boat, Car, and Person in table 3 used parts trained with an old version of consistency, leading to significantly different results. To ensure that the correct detectors were used, all models and classifiers were retrained from scratch, leading to some variance in the results.

Below, shape includes features for aspect ratio, scale, HOG shape classifier, and Object Proposal rank. Reloc indicates the bounding box reestimation described in the paper.

Category aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor meanAP
Reported performance .44340 .35240 .09700 .101 .15120 .44590 .3207 .3534 .0438 .17490 .14960 .27620 .36240 .42080 .3 .0501 .1377 .1877 .34390 .2859 .2499
Parts + Shape + Reloc .44150 .3728 .0927 .0504 .1672 .462 .2851 .3374 .0751 .1695 .1869 .256 .3628 .4211 .2616 .0457 .1663 .1829 .3146 .2886 .2469
Parts + Shape + Texture + Color + Reloc .4465 .3753 .0968 .0452 .1603 .4599 .2843 .3426 .0799 .1656 .1836 .2569 .3627 .4293 .2621 .0586 .1717 .1855 .314 .29 .2484

References

Learning Collections of Part Models for Object Recognition
I Endres, KJ Shih, J Jiaa, D Hoiem
CVPR 2013
[pdf]