Similarity Guided Learning of the Case Description
and Improvement of the System Performance
in an Image Classification System

Petra Perner, Horst Perner, and Bernd Muller

Institute of Computer Vision and Applied Computer Sciences
Arno-Nitzsche-Str. 45, 04277 Leipzig
ibaiperner@aol.com, http://www.ibai-research.de



Abstract. The development of an automatic image classification system is a
hard problem since such a system must imitate the visual strategy of a human
expert when interpreting the particular image. Usually it is not easy to make this
strategy explicit. Rather than describing the visual strategy and the image
features human are able to judge the similarity between the objects. This
judgement can be the basis for a guideline of the development process. This
guideline can help the developer to understand what kind of case
description/features are necessary for a sufficient system performance and can
give an idea what system performance can be achieved. In the paper we
describe a novel strategy which can support a developer in building image
classification systems. The development process as well as the elicitation of the
case description is similarity-guided. Based on the similarity between the
objects the system developer can provide new image features and improve the
system performance until a system performance is reached that fits to the
experts understanding about the relationship among the different objects.
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