Snakes assisted food image segmentation

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 for understanding the link between diet and health. Snakes, or active contours, are used extensively to locate object boundaries and segment images. Experimental results using classical snakes on food images show the problems associated with contour initialization and poor detection performance for food images. In this paper, we explore various methods of contour initialization and integrate a background removal method to improve the performance of food image segmentation. We describe the details of the proposed food image segmentation method and also evaluate our segmentation approach on food images.

[1]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[2]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[3]  K. Flegal,et al.  Diabetes, impaired fasting glucose, and elevated HbA1c in U.S. adolescents: the Third National Health and Nutrition Examination Survey. , 2000, Diabetes care.

[4]  Michel Barlaud,et al.  Using the Shape Gradient for Active Contour Segmentation: from the Continuous to the Discrete Formulation , 2006, Journal of Mathematical Imaging and Vision.

[5]  Frédéric Precioso,et al.  From snakes to region-based active contours defined by region-dependent parameters. , 2004, Applied optics.

[6]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[7]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

[8]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[9]  Edward J. Delp,et al.  Combining global and local features for food identification in dietary assessment , 2011, 2011 18th IEEE International Conference on Image Processing.

[10]  Song Wang,et al.  New benchmark for image segmentation evaluation , 2007, J. Electronic Imaging.

[11]  E J Delp,et al.  Use of technology in children’s dietary assessment , 2009, European Journal of Clinical Nutrition.

[12]  Edward J. Delp,et al.  Integrated database system for mobile dietary assessment and analysis , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[13]  Rémi Ronfard,et al.  Region-based strategies for active contour models , 1994, International Journal of Computer Vision.

[14]  Tom Baranowski,et al.  Need for technological innovation in dietary assessment. , 2010, Journal of the American Dietetic Association.

[15]  Allan D. Jepson,et al.  Quantitative evaluation of a novel image segmentation algorithm , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  David S. Ebert,et al.  Volume estimation using food specific shape templates in mobile image-based dietary assessment , 2011, Electronic Imaging.

[17]  K. Flegal,et al.  Prevalence of high body mass index in US children and adolescents, 2007-2008. , 2010, JAMA.

[18]  David S. Ebert,et al.  The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation , 2010, IEEE Journal of Selected Topics in Signal Processing.

[19]  Allan D. Jepson,et al.  Benchmarking Image Segmentation Algorithms , 2009, International Journal of Computer Vision.

[20]  Jitendra Malik,et al.  An empirical approach to grouping and segmentation , 2002 .

[21]  Kiyoharu Aizawa,et al.  FoodLog: capture, analysis and retrieval of personal food images via web , 2009, CEA '09.