Publication details for Professor Toby BreckonWang, Q., Ning, J. & Breckon, T.P. (2019), A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks, 26th IEEE International Conference on Image Processing (ICIP). Taipei, Taiwan, IEEE, Piscataway, NJ, 644-648.
- Publication type: Conference Paper
- ISSN/ISBN: 2381-8549, 9781538662496
- DOI: 10.1109/ICIP.2019.8803793
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
Recent studies on multi-label image classification have focused
on designing more complex architectures of deep neural
networks such as the use of attention mechanisms and region
proposal networks. Although performance gains have
been reported, the backbone deep models of the proposed
approaches and the evaluation metrics employed in different
works vary, making it difficult to compare fairly. Moreover,
due to the lack of properly investigated baselines, the advantage
introduced by the proposed techniques are often ambiguous.
To address these issues, we make a thorough investigation
of the mainstream deep convolutional neural network
architectures for multi-label image classification and present
a strong baseline. With the use of proper data augmentation
techniques and model ensembles, the basic deep architectures
can achieve better performance than many existing more complex
ones on three benchmark datasets, providing great insight
for the future studies on multi-label image classification.