Image analysis and automatic surface identification by a bi-level multi-classifier

Abstract

Combining the predictions of a set of classifiers has shown to be an effective way of creating composite classifiers that are more accurate than any of the component classifiers; we have performed a research work consisting of the design, development and experimental use of a multi-classifier system for image analysis and surface classification of the different segments that might appear on a given picture in order to help a Mobile Robot in its navigation task. The presented approach combines a number of component classifiers which are standard machine learning classification algorithms, using a second layer paradigm to obtain a better classification accuracy. Experimental results have been obtained using a datafile of cases that contains information about surfaces, extracted from images obtained by the robot. The classification problem consists of recognizing to which of the surfaces belongs a n × n size subimage. The accuracy obtained using the presented new approach statistically improves those obtained using standard machine learning methods. © Springer-Verlag Berlin Heidelberg 2005.