Weakly supervised food image segmentation using class activation maps
© 2017 IEEE. Food image segmentation plays a crucial role in image-based dietary assessment and management. Successful methods for object segmentation generally rely on a large amount of labeled data on the pixel level. However, such training data are not yet available for food images and expensive...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/67520 |
| _version_ | 1848761587200950272 |
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| author | Wang, Y. Zhu, F. Boushey, Carol Delp, E. |
| author_facet | Wang, Y. Zhu, F. Boushey, Carol Delp, E. |
| author_sort | Wang, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 IEEE. Food image segmentation plays a crucial role in image-based dietary assessment and management. Successful methods for object segmentation generally rely on a large amount of labeled data on the pixel level. However, such training data are not yet available for food images and expensive to obtain. In this paper, we describe a weakly supervised convolutional neural network (CNN) which only requires image level annotation. We propose a graph based segmentation method which uses the class activation maps trained on food datasets as a top-down saliency model. We evaluate the proposed method for both classification and segmentation tasks. We achieve competitive classification accuracy compared to the previously reported results. |
| first_indexed | 2025-11-14T10:34:02Z |
| format | Conference Paper |
| id | curtin-20.500.11937-67520 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:34:02Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-675202018-05-18T08:05:32Z Weakly supervised food image segmentation using class activation maps Wang, Y. Zhu, F. Boushey, Carol Delp, E. © 2017 IEEE. Food image segmentation plays a crucial role in image-based dietary assessment and management. Successful methods for object segmentation generally rely on a large amount of labeled data on the pixel level. However, such training data are not yet available for food images and expensive to obtain. In this paper, we describe a weakly supervised convolutional neural network (CNN) which only requires image level annotation. We propose a graph based segmentation method which uses the class activation maps trained on food datasets as a top-down saliency model. We evaluate the proposed method for both classification and segmentation tasks. We achieve competitive classification accuracy compared to the previously reported results. 2018 Conference Paper http://hdl.handle.net/20.500.11937/67520 10.1109/ICIP.2017.8296487 restricted |
| spellingShingle | Wang, Y. Zhu, F. Boushey, Carol Delp, E. Weakly supervised food image segmentation using class activation maps |
| title | Weakly supervised food image segmentation using class activation maps |
| title_full | Weakly supervised food image segmentation using class activation maps |
| title_fullStr | Weakly supervised food image segmentation using class activation maps |
| title_full_unstemmed | Weakly supervised food image segmentation using class activation maps |
| title_short | Weakly supervised food image segmentation using class activation maps |
| title_sort | weakly supervised food image segmentation using class activation maps |
| url | http://hdl.handle.net/20.500.11937/67520 |