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Transferring CNNS to multi-instance multi-label classification on small datasets

Dong, M; Pang, K; Wu, Y; Xue, J-H; Hospedales, T; Ogasawara, T; (2017) Transferring CNNS to multi-instance multi-label classification on small datasets. In: (Proceedings) 24th IEEE International Conference on Image Processing (ICIP). (pp. pp. 1332-1336). IEEE Green open access

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Abstract

Image tagging is a well known challenge in image processing. It is typically addressed through multi-instance multi-label (MIML) classification methodologies. Convolutional Neural Networks (CNNs) possess great potential to perform well on MIML tasks, since multi-level convolution and max pooling coincide with the multi-instance setting and the sharing of hidden representation may benefit multi-label modeling. However, CNNs usually require a large amount of carefully labeled data for training, which is hard to obtain in many real applications. In this paper, we propose a new approach for transferring pre-trained deep networks such as VGG16 on Imagenet to small MIML tasks. We extract features from each group of the network layers and apply multiple binary classifiers to them for multi-label prediction. Moreover, we adopt an L1-norm regularized Logistic Regression (L1LR) to find the most effective features for learning the multi-label classifiers. The experiment results on two most-widely used and relatively small benchmark MIML image datasets demonstrate that the proposed approach can substantially outperform the state-of-the-art algorithms, in terms of all popular performance metrics.

Type: Proceedings paper
Title: Transferring CNNS to multi-instance multi-label classification on small datasets
Event: 24th IEEE International Conference on Image Processing (ICIP)
Location: Beijing, PEOPLES R CHINA
Dates: 17 September 2017 - 20 September 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICIP.2017.8296498
Publisher version: https://doi.org/10.1109/ICIP.2017.8296498
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Imaging Science & Photographic Technology, CNN, Multi-instance, Multi-label, Small dataset, Transfer Learning
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/10050964
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