Efficient superpixel based segmentation for food image analysis
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In this paper, we propose a segmentation method based on normalized cut and superpixels. The method relies on color and texture cues for fast computation and efficient use of memory. The method is used for food image segmentation as part of a mobile food record system we have developed for dietary assessment and management. The accurate estimate of nutrients relies on correctly labelled food items and sufficiently well-segmented regions. Our method achieves competitive results using the Berkeley Segmentation Dataset and outperforms some of the most popular techniques in a food image dataset.
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Zhu, F.; Bosch, M.; Schap, T.; Khanna, N.; Ebert, D.; Boushey, Carol; Delp, E. (2011)Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain ...
He, Y.; Khanna, N.; Boushey, Carol; Delp, E. (2012)In this paper we describe an image segmentation method for segmenting food items in images used for dietary assessment. Dietary assessment methods used to determine the foods and beverages consumed at a meal are essential ...
Chae, J.; Woo, I.; Kim, S.; Maciejewski, R.; Zhu, F.; Delp, E.; Boushey, Carol; Ebert, D. (2011)As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient ...