Dependency-based framework of combining multiple experts for the recognition of unconstrained handwritten numerals

Hee Joong Kang, Seong Whan Lee

Research output: Contribution to journalConference article

9 Citations (Scopus)

Abstract

Although Behavior-Knowledge Space (BKS) method does not need any assumptions in combining multiple experts, it should build theoretically exponential storage spaces for storing and managing jointly observed K decisions from K experts. That is, combining K experts needs a (K+1)st-order probability distribution. However, it is well known that the distribution becomes unmanageable in storing and estimating, even for a small K. In order to overcome such weakness, it would be attractive to decompose the distribution into a number of component distributions and to approximate the distribution with a product of the component distributions. One of such previous works is to apply a conditional independence assumption to the distribution. Another work is to approximate the distribution with a product of only first-order tree dependencies or second-order distributions as shown in [1]. In this paper, a dependency-based framework is proposed to optimally approximate a probability distribution with a product set of dth-order dependencies where 1≤d≤K, and to combine multiple experts based on the product set using the Bayesian formalism. This framework was experimented and evaluated with a standardized CEN-PARMI data base.

Original languageEnglish
Pages (from-to)124-129
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
Publication statusPublished - 1999
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: 1999 Jun 231999 Jun 25

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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