Context based image analysis with application in dietary assessment and evaluation
Dietary assessment is essential for understanding the link between diet and health. We develop a context based image analysis system for dietary assessment to automatically segment, identify and quantify food items from images. In this paper, we describe image segmentation and object classification...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Springer
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/61568 |
| _version_ | 1848760694489481216 |
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| author | Wang, Y. He, Y. Boushey, Carol Zhu, F. Delp, E. |
| author_facet | Wang, Y. He, Y. Boushey, Carol Zhu, F. Delp, E. |
| author_sort | Wang, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Dietary assessment is essential for understanding the link between diet and health. We develop a context based image analysis system for dietary assessment to automatically segment, identify and quantify food items from images. In this paper, we describe image segmentation and object classification methods used in our system to detect and identify food items. We then use context information to refine the classification results. We define contextual dietary information as the data that is not directly produced by the visual appearance of an object in the image, but yields information about a user’s diet or can be used for diet planning. We integrate contextual dietary information that a user supplies to the system either explicitly or implicitly to correct potential misclassifications. We evaluate our models using food image datasets collected during dietary assessment studies from natural eating events. |
| first_indexed | 2025-11-14T10:19:51Z |
| format | Journal Article |
| id | curtin-20.500.11937-61568 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:19:51Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-615682018-08-30T06:44:22Z Context based image analysis with application in dietary assessment and evaluation Wang, Y. He, Y. Boushey, Carol Zhu, F. Delp, E. Dietary assessment is essential for understanding the link between diet and health. We develop a context based image analysis system for dietary assessment to automatically segment, identify and quantify food items from images. In this paper, we describe image segmentation and object classification methods used in our system to detect and identify food items. We then use context information to refine the classification results. We define contextual dietary information as the data that is not directly produced by the visual appearance of an object in the image, but yields information about a user’s diet or can be used for diet planning. We integrate contextual dietary information that a user supplies to the system either explicitly or implicitly to correct potential misclassifications. We evaluate our models using food image datasets collected during dietary assessment studies from natural eating events. 2017 Journal Article http://hdl.handle.net/20.500.11937/61568 10.1007/s11042-017-5346-x Springer restricted |
| spellingShingle | Wang, Y. He, Y. Boushey, Carol Zhu, F. Delp, E. Context based image analysis with application in dietary assessment and evaluation |
| title | Context based image analysis with application in dietary assessment and evaluation |
| title_full | Context based image analysis with application in dietary assessment and evaluation |
| title_fullStr | Context based image analysis with application in dietary assessment and evaluation |
| title_full_unstemmed | Context based image analysis with application in dietary assessment and evaluation |
| title_short | Context based image analysis with application in dietary assessment and evaluation |
| title_sort | context based image analysis with application in dietary assessment and evaluation |
| url | http://hdl.handle.net/20.500.11937/61568 |