Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2015.7351365
Title: Cross-layer features in convolutional neural networks for generic classification tasks
Authors: Peng K.-C.
Chen T. 
Keywords: Convolutional neural networks (CNN)
cross-layer features
generic classification tasks
Issue Date: 2015
Publisher: IEEE Computer Society
Citation: Peng K.-C., Chen T. (2015). Cross-layer features in convolutional neural networks for generic classification tasks. Proceedings - International Conference on Image Processing, ICIP 2015-December : 3057-3061. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2015.7351365
Abstract: Recent works about convolutional neural networks (CNN) show breakthrough performance on various tasks. However, most of them only use the features extracted from the topmost layer of CNN instead of leveraging the features extracted from different layers. As the first group which explicitly addresses utilizing the features from different layers of CNN, we propose cross-layer CNN features which consist of the features extracted from multiple layers of CNN. Our experimental results show that our proposed cross-layer CNN features outperform not only the state-of-the-art results but also the features commonly used in the traditional CNN framework on three tasks - artistic style, artist, and architectural style classification. As shown by the experimental results, our proposed cross-layer CNN features achieve the best known performance on the three tasks in different domains, which makes our proposed cross-layer CNN features promising solutions for generic tasks.
Source Title: Proceedings - International Conference on Image Processing, ICIP
URI: http://scholarbank.nus.edu.sg/handle/10635/146072
ISBN: 9781479983391
ISSN: 15224880
DOI: 10.1109/ICIP.2015.7351365
Appears in Collections:Staff Publications

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