language: en

[Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms Workshop, Thirteenth National Conference on Artificial Intelligence, Portland, OR: AAAI Press (1996).]

Human Expert-Level Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks

Kevin J. Cherkauer
Department of Computer Sciences University of Wisconsin-Madison
1210 West Dayton Street Madison, WI 53706, USA
cherkauer@cs.wisc.edu

Abstract This paper presents the Plannett system, which combines artificial neural networks to achieve expertlevel accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a three-stage image analysis system called JARtool, the end-to-end accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a four-image test suite. JARtool-Plannett also achieves the best algorithmic accuracy on these images to date.

Introduction