Resfeats: Residual network based features for image classification
© 2017 IEEE. Deep residual networks have recently emerged as the state-of-the-art architecture in image classification and object detection. In this paper, we propose new image features (called ResFeats) extracted from the last convolutional layer of the deep residual networks pre-trained on ImageNe...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/70045 |
| _version_ | 1848762200888442880 |
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| author | Mahmood, A. Bennamoun, M. An, Senjian Sohel, F. |
| author_facet | Mahmood, A. Bennamoun, M. An, Senjian Sohel, F. |
| author_sort | Mahmood, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 IEEE. Deep residual networks have recently emerged as the state-of-the-art architecture in image classification and object detection. In this paper, we propose new image features (called ResFeats) extracted from the last convolutional layer of the deep residual networks pre-trained on ImageNet. We propose to use ResFeats for diverse image classification tasks namely, object classification, scene classification and coral classification and show that ResFeats consistently perform better than their CNN counterparts on these classification tasks. Since the ResFeats are large feature vectors, we explore dimensionality reduction methods. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant performance improvement on MIT-67 dataset compared to the widely used CNN features. |
| first_indexed | 2025-11-14T10:43:48Z |
| format | Conference Paper |
| id | curtin-20.500.11937-70045 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:43:48Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-700452018-08-08T04:57:01Z Resfeats: Residual network based features for image classification Mahmood, A. Bennamoun, M. An, Senjian Sohel, F. © 2017 IEEE. Deep residual networks have recently emerged as the state-of-the-art architecture in image classification and object detection. In this paper, we propose new image features (called ResFeats) extracted from the last convolutional layer of the deep residual networks pre-trained on ImageNet. We propose to use ResFeats for diverse image classification tasks namely, object classification, scene classification and coral classification and show that ResFeats consistently perform better than their CNN counterparts on these classification tasks. Since the ResFeats are large feature vectors, we explore dimensionality reduction methods. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant performance improvement on MIT-67 dataset compared to the widely used CNN features. 2018 Conference Paper http://hdl.handle.net/20.500.11937/70045 10.1109/ICIP.2017.8296551 restricted |
| spellingShingle | Mahmood, A. Bennamoun, M. An, Senjian Sohel, F. Resfeats: Residual network based features for image classification |
| title | Resfeats: Residual network based features for image classification |
| title_full | Resfeats: Residual network based features for image classification |
| title_fullStr | Resfeats: Residual network based features for image classification |
| title_full_unstemmed | Resfeats: Residual network based features for image classification |
| title_short | Resfeats: Residual network based features for image classification |
| title_sort | resfeats: residual network based features for image classification |
| url | http://hdl.handle.net/20.500.11937/70045 |