Optimally combining a cascade of classifiers

Kumar Chellapilla; Michael Shilman; Patrice Simard

doi:10.1117/12.643669

16 January 2006 Optimally combining a cascade of classifiers

Kumar Chellapilla, Michael Shilman, Patrice Simard

Proceedings Volume 6067, Document Recognition and Retrieval XIII; 60670Q (2006) https://doi.org/10.1117/12.643669
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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.

Citation Download Citation

Kumar Chellapilla, Michael Shilman, and Patrice Simard "Optimally combining a cascade of classifiers", Proc. SPIE 6067, Document Recognition and Retrieval XIII, 60670Q (16 January 2006); https://doi.org/10.1117/12.643669

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available