Abstract
Diet-related chronic diseases severely affect personal and global health. However, managing or treating these diseases currently requires long training and high personal involvement to succeed. Computer vision systems could assist with the assessment of diet by detecting and recognizing different foods and their portions in images. We propose novel methods for detecting a dish in an image and segmenting its contents with and without user interaction. All methods were evaluated on a database of over 1600 manually annotated images. The dish detection scored an average of 99% accuracy with a .2s/image run time, while the automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 91% respectively, with an average run time of .5s/image, outperforming competing solutions.
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Shroff, G, Smailagic, A., Siewiorek, D.P.: Wearable context-aware food recognition for calorie monitoring. In: 12th IEEE ISWC, pp. 119–120 (2008)
He, Y., Khanna, N., Boushey, C.J., Delp, E.J.: Image segmentation for image-based dietary assessment: a comparative study. In: IEEE ISSCS 2013
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1, 321–331 (1998)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Tran. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Felzenszwalb, P.F., Huttenlocher, D.P.: Image segmentation using local variation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 98–104 (1998)
Zhu, F., Bosch, M., Khanna, N., Boushey, C.J., Delp, E.J.: Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment. IEEE J. Biomed. Health Inform. 19(1), 377–388 (2015)
Matsuda, Y., Hoashi, H., Yanai, K.: Recognition of multiple-food images by detecting candidate regions. In: IEEE ICME, 2012, pp. 25–30 (2012)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Duda, R.O., Hart, P.E.: Use of the Hough Transformation to Detect Lines and Curves in Pictures. Comm. ACM 15, 11–15 (1972)
Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800–810
Bettadapura, V., Thomaz, E., Parnami, A., Abowd, G.D., Essa, I.A.: Leveraging Context to Support Automated Food Recognition in Restaurants. In: WACV 2015, pp. 580–587
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)
Puri, M., Zhu, Z., Yu, Q., Divakaran, A., Sawhney, H.: Recognition and volume estimation of food intake using a mobile device. In: IEEE WACV, pp. 1–8 (2009)
Cai, W., Yu, Q., Wang, H., Zheng, J.: A fast contour-based approach to circle and ellipse detection. In: 5th IEEE WCICA (2004)
Anthimopoulos, M., Dehais, J., Diem, P., Mougiakakou, S.: Segmentation and recognition of multi-food meal images for carbohydrate counting. In: IEEE BIBE (2013)
Kawano, Y., Yanai, K.: FoodCam: A real-time food recognition system on a smartphone. Multimedia Tools and Applications, pp. 1–25 (2014)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)
Morikawa, C., Sugiyama, H., Aizawa, K.: Food region segmentation in meal images using touch points. In: ACM Workshop on Multimedia for Cooking And Eating Activities (2012)
Oliveira, L., Costa, V., Neves, G., Oliveira, T., Jorge, E., Lizarraga, M.: A mobile, lightweight, poll-based food identification system. Pattern Recognition 47, 1941–1952 (2014)
Canny, J.: A Computational Approach to Edge Detetion. IEEE Trans. Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM 24(6), 381–395 (1981)
Ni, K., Jin, H., Dellaert, F.: Groupsac: efficient consensus in the presence of groupings. In: IEEE 12th International Conference on Computer Vision, pp. 2193–2200 (2009)
Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)
Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)
McDonald, R., Smith, K.J.: CIE94a new color difference formula. Journal of the Society of Dyers and Colourists 111(12), 376–379 (1995)
Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Analysis and Machine Intelligence 26(11), 1452–1458 (2004)
Huang, Q., Dom, B.: Quantitative methods of evaluating image segmentation. International Conference on Image Processing 3, 53–56 (1995)
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Dehais, J., Anthimopoulos, M., Mougiakakou, S. (2015). Dish Detection and Segmentation for Dietary Assessment on Smartphones. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_53
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DOI: https://doi.org/10.1007/978-3-319-23222-5_53
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