International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 10, No. 3, 8 Jun. 2020
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Cuisine Detection Using the Convolutional Neural Network
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Author(s)
Dipti Pawade
1
Ashwini Dalvi
1
Irfan Siddavatam
1
Myron Carvalho
1
Prajwal Kotian
1
Hima George
1
Index Terms
Convolutional Neural Network, Cuisine Identification, Nutrition, ReLU.1
Abstract
In today’s fast world, everyone wants the information in one click. The same rule applies when you have some food items in front of you. In social events, few cuisines are known to us while some are not. Also, in a few cases, we know the cuisine name, but we are not aware of its nutritional value. This motivated us to develop a system that can identify the cuisine name from the image and gives the nutrient value for the same. Here Convolutional Neural Network (CNN) is used to predict the cuisine name present in an image and then further its nutritional value is calculated based on the information present in a database. User needs to click the image of the cuisine; the application will identify the cuisine name and its nutrition value for standard serving amount considering the cuisine is prepared using the standard recipe.
Cite This Paper
Dipti Pawade, Ashwini Dalvi, Dr. Irfan Siddavatam, Myron Carvalho, Prajwal Kotian, Hima George. "Cuisine Detection Using the Convolutional Neural Network ", International Journal of Education and Management Engineering(IJEME), Vol.10, No.3, pp.1-11, 2020. DOI: 10.5815/ ijeme.2020.03.01
Reference
[1] P. Chen, Y. Lu, V. W. Zheng, X. Chen, and B. Yang, “KnowEdu : A System to Construct Knowledge Graph for Education,” IEEE, vol. 6, pp. 31553–31563, 2018. Pawade D., Sakhapara A., Shah C., Wala J., Tripathi A., Shah B. (2019) Text Caption Generation Based on Lip Movement of Speaker in Video Using Neural Network. In: Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore
[2] Pawade, A. Sakhapara, M. Jain, N. Jain and K. Gada, "Story Scrambler – Automatic Text Generation Using Word Level RNN-LSTM", International Journal of Information Technology and Computer Science (IJITCS), Vol.10, No.6, pp.44-53, 2018. DOI: 10.5815/ijitcs.2018.06.05
[3] Mezgec, B. Koroušić Seljak, "Nutrinet: A deep learning food and drink image recognition system for dietary assessment", Nutrients, vol. 9, no. 7, pp. 657, 2017.
[4] Bossard L., Guillaumin M., Van Gool L. (2014) Food-101 – Mining Discriminative Components with Random Forests. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8694. Springer, Cham.
[5] R. Kohila and R. Meenakumari, "Predicting calorific value for mixed food using image processing," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2017, pp. 1-4.
[6] P. Pouladzadeh, S. Shirmohammadi and R. Al-Maghrabi, "Measuring Calorie and Nutrition From Food Image," in IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 8, pp. 1947-1956, Aug. 2014.
[7] Kagaya, Hokuto & Aizawa, Kiyoharu & Ogawa, Makoto. (2014). Food Detection and Recognition Using Convolutional Neural Network. 10.13140/2.1.3082.1120.
[8] Amatul Bushra Akhi, Farzana Akter, Tania Khatun & Mohammad Shorif Uddin “Recognition and Classification of Fast Food Images” Global Journal of Computer Science and Technology 2018
[9] Anita Chaudhari, Shraddha More, Sushil Khane, Hemali Mane, Pravin Kamble “Object Detection using Convolutional Neural Network in the Application of Supplementary Nutrition Value of Fruits”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8 Issue-11, September 2019.
[10] Seon-Joo Park, Akmaljon Palvanov, Chang-Ho Lee, Nanoom Jeong, Young-Im Cho, and Hae-Jeung Lee, “The development of food image detection and recognition model of Korean food for mobile dietary management”, Nutr Res Pract. 2019 Dec; 13(6): 521–528.
[11] Ege, T., Yanai, K.: Estimating food calories for multiple-dish food photos. In: Proceedings of Asian Conference on Pattern Recognition (ACPR) (2017).
[12] Horiguchi, S. Amano, M. Ogawa, and K. Aizawa. 2018. Personalized classifier for food image recognition. IEEE Trans. Multimed. 20, 10 (2018), 2836—2848
[13] Gangming Zhao, Zhaoxiang Zhang, He Guan, Peng Tang, Jingdong Wang, “Rethinking ReLU to Train Better CNNs”, 31 Aug 2018. Available on : https://arxiv.org/pdf/1709.06247.pdf
[14] Abien Fred M. Agarap, “Deep Learning using Rectified Linear Units (ReLU)” 7 Feb 2019.Available on : https://arxiv.org/pdf/1803.08375.pdf
[15] Ide, Hidenori & Kurita, Takio. (2017). Improvement of learning for CNN with ReLU activation by sparse regularization. 2684-2691. 10.1109/IJCNN.2017.7966185.
[16] I https://www.nutritionix.com/