Segmentation assisted food classification for dietary assessment
Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate met...
| Main Authors: | , , , , , , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/51147 |
| _version_ | 1848758627028959232 |
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| author | Zhu, F. Bosch, M. Schap, T. Khanna, N. Ebert, D. Boushey, Carol Delp, E. |
| author_facet | Zhu, F. Bosch, M. Schap, T. Khanna, N. Ebert, D. Boushey, Carol Delp, E. |
| author_sort | Zhu, F. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate method for dietary assessment. We are developing methods to identify food items using a single image acquired from the mobile device. Our goal is to automatically determine the regions in an image where a particular food is located (segmentation) and correctly identify the food type based on its features (classification or food labeling). Images of foods are segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from each segmented food region. Classification decisions for each segmented region are made using support vector machine methods. The segmentation of each food region is refined based on feedback from the output of classifier to provide more accurate estimation of the quantity of food consumed. |
| first_indexed | 2025-11-14T09:46:59Z |
| format | Conference Paper |
| id | curtin-20.500.11937-51147 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:46:59Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-511472018-03-29T09:09:27Z Segmentation assisted food classification for dietary assessment Zhu, F. Bosch, M. Schap, T. Khanna, N. Ebert, D. Boushey, Carol Delp, E. Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate method for dietary assessment. We are developing methods to identify food items using a single image acquired from the mobile device. Our goal is to automatically determine the regions in an image where a particular food is located (segmentation) and correctly identify the food type based on its features (classification or food labeling). Images of foods are segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from each segmented food region. Classification decisions for each segmented region are made using support vector machine methods. The segmentation of each food region is refined based on feedback from the output of classifier to provide more accurate estimation of the quantity of food consumed. 2011 Conference Paper http://hdl.handle.net/20.500.11937/51147 10.1117/12.877036 restricted |
| spellingShingle | Zhu, F. Bosch, M. Schap, T. Khanna, N. Ebert, D. Boushey, Carol Delp, E. Segmentation assisted food classification for dietary assessment |
| title | Segmentation assisted food classification for dietary assessment |
| title_full | Segmentation assisted food classification for dietary assessment |
| title_fullStr | Segmentation assisted food classification for dietary assessment |
| title_full_unstemmed | Segmentation assisted food classification for dietary assessment |
| title_short | Segmentation assisted food classification for dietary assessment |
| title_sort | segmentation assisted food classification for dietary assessment |
| url | http://hdl.handle.net/20.500.11937/51147 |