Improving Image Classification by Combining Statistical, Case-Based and Model-Based Prediction Methods

Article type: Research Article

Authors: Clark, Peter | Feng, Cao | Matwin, Stan | Fung, Ko

Affiliations: Ottawa Machine Learning Group, Computer Science, University of Ottawa, Ottawa KIN 6N5, Canada. {pclark,cfeng,sian}@csi.uottawa.ca | Canada Centre for Remote Sensing, 588 Booth St., Ottawa K1A 0Y7 Canada. fung@ccrs.emr.ca

Abstract: Evidence for image classification can be considered to come from two sources: traditional statistical information derived algorithmically from image data, and model-based evidence arising from previous expertise and experience in a given application domain. This paper presents a study of classification techniques based on both these sources (traditional algorithmic and model-based), and illustrates how they can be combined. A prototype image classification system, called Cabaress, has been constructed which implements these methods. We evaluate Cabaress as applied to the problem of identifying crops in agricultural fields, based on classifying image segments extracted from radar image data. Our results demonstrate this mixed-method approach can achieve improved classificational accuracy.

DOI: 10.3233/FI-1997-303401

Journal: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 227-240, 1997