Reverse Training for Leaf Image Set Classification

Abstract

This paper presents a new approach for leaf image set classification, where each training and testing set contains many image instances of a leaf. This approach efficiently extends binary classifiers for the task of multi-class image set classification. First, the training set is divided into two part using clustering algorithms: one will train a classifier with the images of the query set; the rest of the training set will evaluate the trained classifier and then predict the class of the query image set. The PHOG feature and Gist feature of leaf image set are merged into the whole feature of leaf image sets. Extensive experiments and comparisons with existing methods show that the proposed approach achieves state of the art performance for leaf image set recognition.