The Community for Technology Leaders
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)
San Francisco, CA, USA
June 13, 2010 to June 18, 2010
ISBN: 978-1-4244-6984-0
pp: 1006-1013
Sudheendra Vijayanarasimhan , University of Texas at Austin, Microsoft Research Redmond
Ashish Kapoor , University of Texas at Austin, Microsoft Research Redmond
ABSTRACT
Visual recognition and detection are computationally intensive tasks and current research efforts primarily focus on solving them without considering the computational capability of the devices they run on. In this paper we explore the challenge of deriving methods that consider constraints on computation, appropriately schedule the next best computation to perform and finally have the capability of producing reasonable results at any time when a solution is required. We specifically derive an approach for the task of object category localization and classification in cluttered, natural scenes that can not only produce anytime results but also utilize the principle of value-of-information in order to provide the most recognition bang for the computational buck. Experiments on two standard object detection challenges show that the proposed framework can triage computation effectively and attain state-of-the-art results when allowed to run till completion. Additionally, the real benefit of the proposed framework is highlighted in the experiments where we demonstrate that the method can provide reasonable recognition results even if the procedure needs to terminate before completion.
INDEX TERMS
CITATION

A. Kapoor and S. Vijayanarasimhan, "Visual recognition and detection under bounded computational resources," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), San Francisco, CA, USA, 2010, pp. 1006-1013.
doi:10.1109/CVPR.2010.5540109
101 ms
(Ver 3.3 (11022016))