/Data-driven Exemplar Model Selection

Data-driven Exemplar Model Selection

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

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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.

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