Image segmentation for image-based dietary assessment: A comparative study
There is a health crisis in the US related to diet that is further exacerbated by our aging population and sedentary lifestyles. Six of the ten leading causes of death in the United States can be directly linked to diet. Dietary assessment, the process of determining what someone eats during the cou...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/49949 |
| _version_ | 1848758357471526912 |
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| author | He, Y. Khanna, N. Boushey, Carol Delp, E. |
| author_facet | He, Y. Khanna, N. Boushey, Carol Delp, E. |
| author_sort | He, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | There is a health crisis in the US related to diet that is further exacerbated by our aging population and sedentary lifestyles. Six of the ten leading causes of death in the United States can be directly linked to diet. Dietary assessment, the process of determining what someone eats during the course of a day, is essential for understanding the link between diet and health. We are developing imaging based tools to automatically obtain accurate estimates of what foods a user consumes. Accurate food segmentation is essential for identifying food items and estimating food portion sizes. In this paper, we present a quantitative evaluation of automatic image segmentation methods for food image analysis used for dietary assessment. The experiments indicate that local variation is more suitable for food image segmentation in general dietary assessment studies where the food images acquired have complex background. |
| first_indexed | 2025-11-14T09:42:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-49949 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:42:42Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-499492018-03-29T09:07:08Z Image segmentation for image-based dietary assessment: A comparative study He, Y. Khanna, N. Boushey, Carol Delp, E. There is a health crisis in the US related to diet that is further exacerbated by our aging population and sedentary lifestyles. Six of the ten leading causes of death in the United States can be directly linked to diet. Dietary assessment, the process of determining what someone eats during the course of a day, is essential for understanding the link between diet and health. We are developing imaging based tools to automatically obtain accurate estimates of what foods a user consumes. Accurate food segmentation is essential for identifying food items and estimating food portion sizes. In this paper, we present a quantitative evaluation of automatic image segmentation methods for food image analysis used for dietary assessment. The experiments indicate that local variation is more suitable for food image segmentation in general dietary assessment studies where the food images acquired have complex background. 2013 Conference Paper http://hdl.handle.net/20.500.11937/49949 10.1109/ISSCS.2013.6651268 restricted |
| spellingShingle | He, Y. Khanna, N. Boushey, Carol Delp, E. Image segmentation for image-based dietary assessment: A comparative study |
| title | Image segmentation for image-based dietary assessment: A comparative study |
| title_full | Image segmentation for image-based dietary assessment: A comparative study |
| title_fullStr | Image segmentation for image-based dietary assessment: A comparative study |
| title_full_unstemmed | Image segmentation for image-based dietary assessment: A comparative study |
| title_short | Image segmentation for image-based dietary assessment: A comparative study |
| title_sort | image segmentation for image-based dietary assessment: a comparative study |
| url | http://hdl.handle.net/20.500.11937/49949 |