A versatile framework for labelling imagery with a large number of classes

Abstract

Conventional methods for feature selection use some kind of separability criteria or classification accuracy for computing the relevance of a feature subset to the classification task. In two-class problems, this approach may be suitable, but for problems such as character recognition with 26 classes, these feature selection algorithms are often faced with complex tradeoffs among efficacy of features for separating different subsets of classes. We propose a class-pair based feature selection algorithm which, in conjunction with mixture modeling technique, provides significantly superior results for differentiating a large number of classes, even when the class priors vary considerably. This technique is applied to multisensor NASA/JPL remote sensing AIRSAR data for characterizing 11 types of land cover. The proposed polychotomous approach not only gives improved test accuracy, but also reduces the number of features used. Important domain information can be derived from the features selected for different class pairs and the distance measure between these class pairs.