A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks

Wang, Q.; Ning, J.; Breckon, T.P.

Abstract

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.

Citation

Wang, Q., Ning, J., & Breckon, T. (2019, September). A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks. Presented at 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 26th IEEE International Conference on Image Processing (ICIP)
• Start Date: Sep 22, 2019
• End Date: Sep 25, 2019
• Acceptance Date: Apr 30, 2019
• Publication Date: 2019
• Deposit Date: Jun 4, 2019
• Publicly Available Date: Nov 12, 2019
• Pages: 644-648
• Series ISSN: 2381-8549
• Book Title: 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings.
• DOI: https://doi.org/10.1109/icip.2019.8803793
• Public URL: https://durham-repository.worktribe.com/output/1142739