Feature space reduction for graph-based image classification

Abstract

Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the benefits of features selections are needed. This is due using frequent approximate subgraphs (FAS) leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50% of the original features and we get similar classification results than those reported by using all the features. © Springer-Verlag 2013.