Food region segmentation in meal images using touch points

Authors:
Chamin Morikawa;Haruki Sugiyama;Kiyoharu Aizawa
Affiliations:
The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan
Venue:
Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities
Year:
2012

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Abstract

We propose an interactive scheme for segmenting meal images for automated dietary assessment. A smartphone user photographs a meal and marks a few touch points on the resulting image. The segmentation algorithm initializes a set of food segments with the touch points, and grows them using local image features. We evaluate the algorithm with a data set consisting of 300 manually segmented meal images. The precision of segmentation is 0.87, compared with 0.70 for fully automatic segmentation. The results show that the precision of segmentation was significantly improved by incorporating minimal user intervention.