Distinguishing Nigerian Food Items and Calorie Content with Hyperspectral Imaging. Wang, X., Rohani, N., Manerikar, A., Katsagellos, A., Cossairt, O., & Alshurafa, N. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10590 LNCS, pages 462–470, 2017. ![Distinguishing Nigerian Food Items and Calorie Content with Hyperspectral Imaging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Identifying food types consumed and their calorie composition is one of the central tasks of dietary assessment. Traditional automated image processing methods learn to map images to an existing food database with known caloric composition. However, even when the correct food type is identified, caloric makeup can vary depending on its ingredients, and using true-color images proves insufficient to distinguish within food type variability. In this paper, we show that hyperspectral imaging provides useful information and promise in distinguishing caloric composition within the same food type. We collect data using a hyperspectral camera from Nigerian foods cooked with varying degrees of fat content, and capture images under different intensities of light. We apply Principle Component Analysis (PCA) to reduce the dimensionality, and train a Support Vector Machine (SVM) classifier using a Radial Basis Function kernel and show that applying this technique on hyperspectral images can more readily distinguish calorie composition. Furthermore, compared with methods that only use true-color based features, our method shows that a classifier trained using features from hyperspectral images is significantly more predictive of within-food caloric content, and by fusing results from two classifiers trained separately using hyperspectral and RGB imagery we obtain the greatest predictive power.
@inproceedings{Manerikar2017,
abstract = {Identifying food types consumed and their calorie composition is one of the central tasks of dietary assessment. Traditional automated image processing methods learn to map images to an existing food database with known caloric composition. However, even when the correct food type is identified, caloric makeup can vary depending on its ingredients, and using true-color images proves insufficient to distinguish within food type variability. In this paper, we show that hyperspectral imaging provides useful information and promise in distinguishing caloric composition within the same food type. We collect data using a hyperspectral camera from Nigerian foods cooked with varying degrees of fat content, and capture images under different intensities of light. We apply Principle Component Analysis (PCA) to reduce the dimensionality, and train a Support Vector Machine (SVM) classifier using a Radial Basis Function kernel and show that applying this technique on hyperspectral images can more readily distinguish calorie composition. Furthermore, compared with methods that only use true-color based features, our method shows that a classifier trained using features from hyperspectral images is significantly more predictive of within-food caloric content, and by fusing results from two classifiers trained separately using hyperspectral and RGB imagery we obtain the greatest predictive power.},
author = {Wang, Xinzuo and Rohani, Neda and Manerikar, Adwaiy and Katsagellos, Aggelos and Cossairt, Oliver and Alshurafa, Nabil},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-70742-6_45},
isbn = {9783319707419},
issn = {16113349},
keywords = {Calorie detection,Food identification,Hyperspectral imaging},
pages = {462--470},
title = {{Distinguishing Nigerian Food Items and Calorie Content with Hyperspectral Imaging}},
url = {https://link.springer.com/10.1007/978-3-319-70742-6_45},
volume = {10590 LNCS},
year = {2017}
}
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We collect data using a hyperspectral camera from Nigerian foods cooked with varying degrees of fat content, and capture images under different intensities of light. We apply Principle Component Analysis (PCA) to reduce the dimensionality, and train a Support Vector Machine (SVM) classifier using a Radial Basis Function kernel and show that applying this technique on hyperspectral images can more readily distinguish calorie composition. 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