2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 7 , Issue 2 , PP: 51-59, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Upma Kumari 1 * , Praveen Gupta 2 , Chaur Singh Rajpoot 3
Doi: https://doi.org/10.54216/JISIoT.070205
Food image recognition system has various applications now a day. In this paper, we have used a machine learning supervised approach and Support Vector Machine to classify different food images. SVM has been classified to detect and recognize food images with the least modification. By applying various filters like a texture filter, a segmentation method, clustering, and a SVM approach we have achieved more accuracy than other machine learning approaches with manually extracting features. Sustenance is an indivisible piece of people groups lives. we tend to apply a convolution neural network (CNN) to the undertakings of analyst work and perceiving sustenance pictures. Clarification for the wide decent variety of styles of nourishment, and picture acknowledgment of sustenance things are typically unpleasant difficulties. Nevertheless, profound learning has been demonstrated starting late to be a genuinely extreme picture acknowledgment framework, and CNN could be a dynamic approach to managing profound learning. CNN showed on a very basic level higher precision than did old-fashioned help vector-machine-based courses with carefully assembled decisions. For sustenance picture disclosure, CNN likewise demonstrated fundamentally count higher precision than a standard technique. Generally higher precision than standard techniques.
CNN , texture filter , k-mean clustering , segmentation
[1] Aizawa, K., Maruyama, Y., Li, H., & Morikawa, C. (2013). Food Balance Estimation by Using Personal Dietary Tendencies in a Multimedia Food Log. IEEE Transactions on Multimedia, 15(8), 2176–2185. https://doi.org/10.1109/TMM.2013.2271474
[2] Bosch, M., Zhu, F., Khanna, N., Boushey, C. J., & Delp, E. J. (2011). Combining global and local features for food identification in dietary assessment. 2011 18th IEEE International Conference on Image Processing, 1789–1792. https://doi.org/10.1109/ICIP.2011.6115809
[3] Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., & Yang, J. (2009). PFID: Pittsburgh fast-food image dataset. 2009 16th IEEE International Conference on Image Processing (ICIP), 289 –292. https://doi.org/10.1109/ICIP.2009.5413511
[4] Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J. -J., Sandhu, S., Guppy, K. H., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. The American Journal of Cardiology, 64(5), 304–310. https://doi.org/10.1016/0002-9149(89)90524-9
[5] Devi, A. D. (2015). Enhanced Prediction of Heart Disease by Genetic. International Journal of Advanced Information in Engineering Technology (IJAIET), 2(2).
[6] Gupta, P., & Kaur, B. (2014). Accuracy Enhancement of Artificial Neural Network using Genetic Algorithm. International Journal of Computer Applications, 103(13), 11–15.
[7] He, H., Kong, F., & Tan, J. (2016). DietCam: Multiview Food Recognition Using a Multikernel SVM. IEEE Journal of Biomedical and Health Informatics, 20(3), 848–855. https://doi.org/10.1109/JBHI.2015.2419251
[8] Kawano, Y., & Yanai, K. (2013). Real-Time Mobile Food Recognition System. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 1–7. https://doi.org/10.1109/CVPRW.2013.5
[9] Liew Pol Yee, & De Silva, L. C. (2002). Application of MultiLayer Perceptron Network as a one-way hash function. Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), 1459–1462. https://doi.org/10.1109/IJCNN.2002.1007732
[10] Singh, S., & Bansal, R. K. (2012). International Journal of Advanced Research in Computer Science and Software Engineering. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3, March 2012. http://www.ijarcsse.com/
[11] Velvizhy, P., Pavithra, & Kannan, A. (2014). Automatic food recognition system for diabetic patients. 2014 Sixth International Conference on Advanced Computing (ICoAC), 329–334. https://doi.org/10.1109/ICoAC.2014.7229735
[12] Zhang, W., Zhao, D., Gong, W., Li, Z., Lu, Q., & Yang, S. (2015). Food Image Recognition with Convolutional Neural Networks. 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 690–693. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.139
[13] P. Gupta, C. S. Rajpoot, T. S. Shanthi, D. Prasad, A. Kumar, and S. S. Kumar, "Image Forgery Detection using Deep Learning Model," 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2022, pp. 1256-1262, doi: 10.1109/ICOSEC54921.2022.9951952.
