Modeling and estimation of spatial random trees with application to image classification

Author

Pollak, I. ; Siskind, J.M. ; Harpe, M.P. ; Bouman, C.A.

Author_Institution

Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

3

fYear

2003

fDate

6-10 April 2003

Abstract

A new class of multiscale multidimensional stochastic processes called spatial random trees is introduced. The model is based on multiscale stochastic trees with stochastic structure as well as stochastic states. Procedures are developed for exact likelihood calculation, MAP estimation of the process, and estimation of the parameters of the process. The new framework is illustrated through a simple binary image classification problem.

Keywords

image classification; maximum likelihood estimation; stochastic processes; trees (mathematics); MAP estimation; binary image classification; exact likelihood calculation; multiscale multidimensional stochastic processes; multiscale stochastic trees; parameter estimation; spatial random trees estimation; spatial random trees modeling; stochastic states; stochastic structure; Application software; Character recognition; Engineering profession; Image classification; Multidimensional systems; Optical character recognition software; Parameter estimation; State estimation; Stochastic processes; Tree data structures;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-7663-3

Type

conf

DOI

10.1109/ICASSP.2003.1199466

Filename

1199466