Reference: Improving Image Classification by Combining Statistical, Case-Based and Model-Based Prediction Methods. Fundamenta Informaticae, 30 (3/4), pages 227-240 (1997). Also available as Technical Report TR-93-00, Dept. of Computer Science, Univ. Ottawa, 1993.
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.