NDLI: Data-driven exemplar model selection

Content Provider	IEEE Xplore Digital Library
Author	Misra, I. Shrivastava, A. Hebert, M.
Copyright Year	2014
Description	Author affiliation: Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA (Misra, I.; Shrivastava, A.; Hebert, M.)
Abstract	We consider the problem of discovering discriminative exemplars suitable for object detection. Due to the diversity in appearance in real world objects, an object detector 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 training images to capture such variations. Current methods do not scale to large training sets either because of training 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 compact set of exemplar models for object detection. These selected 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.
Sponsorship	IEEE Comput. Soc.
Starting Page	339
Ending Page	346
File Size	4716148
Page Count	8
File Format	PDF
ISBN	9781479949854
DOI	10.1109/WACV.2014.6836080
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Training Redundancy Measurement Object detection Libraries Sparse matrices
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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