GMM Improves the Reject Option in Hierarchical Classification for Fish Recognition

Abstract

A reject option in classification is useful to filter less confident decisions of known classes or to detect and remove untrained classes. This paper presents a novel rejection system in a hierarchical classification method for fish species recognition. Since hierarchical methods accumulate errors along the decision path, the rejection system provides an alternative channel to discover misclassified samples at the leaves of the classification hierarchy. This is also applied to probe test samples from new classes. We apply a Gaussian Mixture Model (GMM) to evaluate the posterior probability of testing samples. 2626 dimensions of features, e.g. color and shape and texture properties, from different parts of the fish are computed and normalized. We use forward sequential feature selection (FSFS), which utilizes SVM as a classifier, to select a subset of effective features that distinguishes samples of a given class from others. After learning the mixture models, the reject function is integrated with a Balance-Guaranteed Optimized Tree (BGOT) hierarchical method. We compare three rejection methods. The experimental results demonstrate a reduction in the accumulated errors from hierarchical classification and an improvement in discovering unknown classes.