Combining global and local features for food identification in dietary assessment

Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we de...

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Bibliographic Details
Published in2011 18th IEEE International Conference on Image Processing Vol. 2011; pp. 1789 - 1792
Main Authors Bosch, M., Zhu, F., Khanna, N., Boushey, C. J., Delp, E. J.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.09.2011
Subjects
Color
Conferences
Feature extraction
image analysis
Image color analysis
Image segmentation
image texture
object recognition
supervised learning
Vectors
Visualization
object recognition
Feature extraction
image analysis
supervised learning
image texture
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Summary:Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures and a "voting" based late decision fusion classifier to identify the food items. Experimental results on a wide variety of food items are presented.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISBN:1457713047
9781457713040
ISSN:1522-4880
2381-8549
1057-7149
2381-8549
1941-0042
DOI:10.1109/ICIP.2011.6115809