Clustering-Based Hierarchical Framework for Multiclass Classification of Leaf Images

Abstract

This article introduces a multiclass classification approach accustoming the benefits of partitioning-based strategies and hierarchical techniques. The proposed hierarchical framework creates a hierarchy with the notion of grouping classes with similar traits as one group. It overcomes the deficiency of the existing multiclass extension approaches, viz., nonlinearity, imbalanced class classification, and increasing classification cost with increasing number of classes. The hierarchical framework presents the idea of decomposing several classes hierarchically, where every cluster contains a set of classes having similar traits. The approach aims to maximize the intercluster distance and minimize the intracluster distribution. The effectiveness of the proposed method is evaluated on real-world and complex problems of plant recognition. Three leaf image datasets are considered for performance evaluation using a support vector machine. The results signify that the proposed approach for multiclass classification is an efficient approach with significantly improved recognition accuracy. It is a robust and effective approach with the least computational cost. The speedup factor of the proposed approach in the binary structure is 16, 6.5, and 5.5 as compared to a one-versus-one traditional support vector machine for Flavia, Swedish, and self-collected leaf datasets, respectively.