Data-driven Exemplar Model Selection

Ishan Misra, Abhinav Shrivastava, and Martial Hebert
Winter Conference on Applications of Computer Vision (WACV), March, 2014.


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Abstract
We consider the problem of discovering discriminative exemplars suitable for object detection. Due to the diver- sity in appearance in real world objects, an object detec- tor must capture variations in scale, viewpoint, illumination etc. The current approaches do this by using mixtures of models, where each mixture is designed to capture one (or a few) axis of variation. Current methods usually rely on heuristics to capture these variations; however, it is unclear which axes of variation exist and are relevant to a particular task. Another issue is the requirement of a large set of train- ing images to capture such variations. Current methods do not scale to large training sets either because of train- ing time complexity [31] or test time complexity [26]. In this work, we explore the idea of compactly capturing task- appropriate variation from the data itself. We propose a two stage data-driven process, which selects and learns a com- pact set of exemplar models for object detection. These se- lected models have an inherent ranking, which can be used for anytime/budgeted detection scenarios. Another benefit of our approach (beyond the computational speedup) is that the selected set of exemplar models performs better than the entire set.

Notes

Text Reference
Ishan Misra, Abhinav Shrivastava, and Martial Hebert, "Data-driven Exemplar Model Selection," Winter Conference on Applications of Computer Vision (WACV), March, 2014.

BibTeX Reference
@inproceedings{Misra_2014_7568,
   author = "Ishan Misra and Abhinav Shrivastava and Martial Hebert",
   title = "Data-driven Exemplar Model Selection",
   booktitle = "Winter Conference on Applications of Computer Vision (WACV)",
   month = "March",
   year = "2014",
}