Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
Abstract
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.
Cite
Text
Ge et al. "Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477700Markdown
[Ge et al. "Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/ge2016wacv-fine/) doi:10.1109/WACV.2016.7477700BibTeX
@inproceedings{ge2016wacv-fine,
title = {{Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks}},
author = {Ge, ZongYuan and Bewley, Alex and McCool, Christopher and Corke, Peter I. and Upcroft, Ben and Sanderson, Conrad},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-6},
doi = {10.1109/WACV.2016.7477700},
url = {https://mlanthology.org/wacv/2016/ge2016wacv-fine/}
}