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Proceedings Paper

Optimally combining a cascade of classifiers
Author(s): Kumar Chellapilla; Michael Shilman; Patrice Simard
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Paper Abstract

Conventional approaches to combining classifiers improve accuracy at the cost of increased processing. We propose a novel search based approach to automatically combine multiple classifiers in a cascade to obtain the desired tradeoff between classification speed and classification accuracy. The search procedure only updates the rejection thresholds (one for each constituent classier) in the cascade, consequently no new classifiers are added and no training is necessary. A branch-and-bound version of depth-first-search with efficient pruning is proposed for finding the optimal thresholds for the cascade. It produces optimal solutions under arbitrary user specified speed and accuracy constraints. The effectiveness of the approach is demonstrated on handwritten character recognition by finding a) the fastest possible combination given an upper bound on classification error, and also b) the most accurate combination given a lower bound on speed.

Paper Details

Date Published: 16 January 2006
PDF: 8 pages
Proc. SPIE 6067, Document Recognition and Retrieval XIII, 60670Q (16 January 2006); doi: 10.1117/12.643669
Show Author Affiliations
Kumar Chellapilla, Microsoft Corp. (United States)
Michael Shilman, Microsoft Corp. (United States)
Patrice Simard, Microsoft Corp. (United States)

Published in SPIE Proceedings Vol. 6067:
Document Recognition and Retrieval XIII
Kazem Taghva; Xiaofan Lin, Editor(s)

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