Distinguishing Nigerian Food Items and Calorie Content with Hyperspectral Imaging

Xinzuo Wang, Neda Rohani, Adwaiy Manerikar, Aggelos Katsagellos, Oliver Strides Cossairt, Nabil Alshurafa*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationNew Trends in Image Analysis and Processing – ICIAP 2017 - ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Revised Selected Papers
EditorsSebastiano Battiato, Giovanni Maria Farinella, Marco Leo, Giovanni Gallo
PublisherSpringer Verlag
Pages462-470
Number of pages9
ISBN (Print)9783319707419
DOIs
StatePublished - 2017
Event19th International Conference on Image Analysis and Processing, ICIAP 2017 - Catania, Italy
Duration: Jun 5 2017Jun 9 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10590 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Image Analysis and Processing, ICIAP 2017
Country/TerritoryItaly
CityCatania
Period6/5/176/9/17

Keywords

  • Calorie detection
  • Food identification
  • Hyperspectral imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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