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arXiv · 1511.09209

Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks

ZongYuan Ge·Alex Bewley·Christopher McCool·Ben Upcroft·Peter Corke·Conrad Sanderson
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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.

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ZongYuan Ge, Alex Bewley, Christopher McCool, Ben Upcroft, Peter Corke, Conrad Sanderson. 2015-11-30. Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks. https://arxiv.org/abs/1511.09209

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