A hierarchical class-grouping approach, and a study of classification strategies for leaf classification

Fine-grained visual classification has been considered for image data in various domains of environmental importance such as birds, animals and plants. This work considers the classification problem of the latter, based on the leaf shape. Traditional works in such areas typically propose better features, or sophisticated classification frameworks. In this work, we ask a different question: Given simple and efficient features, and a well-known binary classifier such as support vector machine (SVM), among various strategies, what may be a good way to pose the multi-class classification problem as multiple binary classifications? In this respect, we compare three different strategies, all of which use the same set of features. From our results, we conclude that, one of these three approaches, based on hierarchical class-grouping, clearly outperforms the others, with high classification accuracy. This suggest that classification strategy is an important aspect for the given features and classifiers. To our knowledge, such a study in the fine-grained classification area (and particularly for the nascent area of leaf-classification), has not yet been explored.