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.
Original language | English |
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Title of host publication | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV 2016) |
Editors | Greg Mori, Robert Pless, Scott McCloskey, Rahul Sukthankar |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISBN (Electronic) | 9781509006410 |
ISBN (Print) | 9781509006427 |
DOIs | |
Publication status | Published - 23 May 2016 |
Externally published | Yes |
Event | IEEE Winter Conference on Applications of Computer Vision 2016 - Lake Placid, United States of America Duration: 7 Mar 2016 → 10 Mar 2016 http://www.wacv16.org/ http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=46372©ownerid=26710 https://ieeexplore.ieee.org/xpl/conhome/7469250/proceeding (Proceedings) |
Conference
Conference | IEEE Winter Conference on Applications of Computer Vision 2016 |
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Abbreviated title | WACV 2016 |
Country | United States of America |
City | Lake Placid |
Period | 7/03/16 → 10/03/16 |
Internet address |