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Abstract

This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearest-neighbor approach. The method is based on training a separate linear SVM classifier for every exemplar in the training set. Each of these Exemplar-SVMs is thus defined by a single positive instance and millions of negatives. While each detector is quite specific to its exemplar, we empirically observe that an ensemble of such Exemplar-SVMs offers surprisingly good generalization. Our performance on the PASCAL VOC detection task is on par with the much more complex latent part-based model of Felzenszwalb et al., at only a modest computational cost increase. But the central benefit of our approach is that it creates an explicit association between each detection and a single training exemplar. Because most detections show good alignment to their associated exemplar, it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding.

Keywords

object detection, machine learning, geometry transfer

Notes

Text Reference

Tomasz Malisiewicz, Abhinav Gupta, and Alexei A. Efros, "Ensemble of Exemplar-SVMs for Object Detection and Beyond," International Conference of Computer Vision, November, 2011.

BibTeX Reference

@inproceedings{Malisiewicz_2011_6920,
   author = "Tomasz Malisiewicz and Abhinav Gupta and Alexei A. Efros",
   title = "Ensemble of Exemplar-SVMs for Object Detection and Beyond",
   booktitle = "International Conference of Computer Vision",
   month = "November",
   year = "2011",
}

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