Image segmentation and classification with application to dietary assessment using BMI-calorie calculator
Abstract
Nowadays, people are more interested in their health by maintaining a proper diet. Today’s lifestyle causes obesity and malnutrition in humans because of an uncontrolled diet. This paper proposes the health monitoring system using the body mass index (BMI) calorie calculator, which guides people to take proper calories from their daily diet. The image processing steps segmentation, features extraction, and recognition are used in the dietary assessment to identify the food items. The improved performance of the multi-hypotheses image segmentation (MHS) and feed-forward neural network (FFNN) classifier for nutritional assessment was evaluated using macro average accuracy (MAA) and standard accuracy (SA) metrics, which provide an enhanced classification rate.
Keywords:
segmentation, feature extraction, classification, calorie estimationReferences
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[6] W.T. Freeman, E.H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9): 891–906, 1991, https://doi.org/10.1109/34.93808.
[7] D.G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91–110, 2004, https://doi.org/10.1023/B:VISI.0000029664.99615.94.
[8] S.J. Minija, W.R.S. Emmanuel. Neural network classifier and multiple hypothesis image segmentation for dietary assessment using calorie calculator. The Imaging Science Journal, 65(7): 379–392, 2017, https://doi.org/10.1080/13682199.2017.1356610.
[9] P. Pouladzadeh, G. Villalobos, R. Almaghrabi, S. Shirmohammadi. A novel SVM based food recognition method for calorie measurement applications. [In:] Proceedings of 2012 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 495–498, 2012.
[10] M.H. Rahman et al. Food volume estimation in a mobile phone based dietary assessment system. [In:] 2012 8th IEEE International Conference on Signal Image Technology and Internet Based Systems, pp. 988–995, 25–29, Nov. 2012, Naples, Italy, https://doi.org/10.1109/SITIS.2012.146.
[11] K. Saravanan, S. Sasithra. Review on classification based on artificial neural networks. International Journal of Ambient Systems and Applications, 2(4): 11–18, 2014, https://doi.org/10.5121/ijasa.2014.2402.
[12] B.L. Six et al. Evidence-based development of a mobile telephone food record. Journal of the American Dietetic Association, 110(1): 74–79, 2010, https://doi.org/10.1016/j.jada.2009.10.010.
[13] F. Zhu, M. Bosch, N. Khanna, C.J. Boushey, E.J. Delp. Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE Journal of Biomedical and Health Informatics, 19(1): 377–388, 2015, https://doi.org/10.1109/JBHI.2014.2304925.
[14] A. Biem, S. Katagiri. Feature extraction based on minimum classification error/generalized probabilistic descent method. [In:] Proceedings of IEEE International Conference on Acoustic, Speech, Signal Process, pp. 275–278, Apr. 1993, https://doi.org/10.1109/ICASSP.1993.319289.
