We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN for each subset. The output from each of the K DCNNs is combined to form a single classification decision. In contrast to previous techniques, we provide a formulation to perform joint end-to-end training of the K DCNNs simultaneously. Extensive experiments, on three datasets using two network structures (AlexNet and GoogLeNet), show that the proposed MixDCNN system consistently outperforms other methods. It provides a relative improvement of 12.7% and achieves state-of-the-art results on two datasets.
@article{arxiv.1511.09209,
title = {Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks},
author = {ZongYuan Ge and Alex Bewley and Christopher McCool and Ben Upcroft and Peter Corke and Conrad Sanderson},
journal= {arXiv preprint arXiv:1511.09209},
year = {2015}
}