Analysis of food images: Features and classification
In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquire...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/50858 |
| _version_ | 1848758553359155200 |
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| author | He, Y. Xu, C. Khanna, N. Boushey, Carol Delp, E. |
| author_facet | He, Y. Xu, C. Khanna, N. Boushey, Carol Delp, E. |
| author_sort | He, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy. |
| first_indexed | 2025-11-14T09:45:49Z |
| format | Conference Paper |
| id | curtin-20.500.11937-50858 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:45:49Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-508582018-03-29T09:09:27Z Analysis of food images: Features and classification He, Y. Xu, C. Khanna, N. Boushey, Carol Delp, E. In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy. 2014 Conference Paper http://hdl.handle.net/20.500.11937/50858 10.1109/ICIP.2014.7025555 Institute of Electrical and Electronics Engineers Inc. restricted |
| spellingShingle | He, Y. Xu, C. Khanna, N. Boushey, Carol Delp, E. Analysis of food images: Features and classification |
| title | Analysis of food images: Features and classification |
| title_full | Analysis of food images: Features and classification |
| title_fullStr | Analysis of food images: Features and classification |
| title_full_unstemmed | Analysis of food images: Features and classification |
| title_short | Analysis of food images: Features and classification |
| title_sort | analysis of food images: features and classification |
| url | http://hdl.handle.net/20.500.11937/50858 |