Food Recognition and Calorie Assessment from Images Using Convolutional Neural Networks

Authors

  • Yogeshvari Makwana, Sailesh Iyer, Sanju Tiwari

Keywords:

Food recognition, Deep convolutional neural network, Artificial neural network, Calorie Assessment, Food Segmentation, Food classification, Image processing, Computer vision,Calorie Estimation

Abstract

In human life there are many things that are required to live but there are three basic needs of every human. These three needs are a must in every human life that are food, clothes and house. Nowadays humans are very much interested in eating different types of foods. Interested to know different kinds of foods from different places. We have developed a food recognition and calorie estimation system that utilises images of food provided by the user to recognize the food item and estimate its calorie content. Food image recognition is a promising application of visual object recognition in computer vision. Our system leverages image processing techniques and computational intelligence to accurately recognize food items. We have trained a large and deep convolutional neural network using a dataset of 1000 high-resolution images for each food category. The trained CNN is capable of classifying the input food images with high accuracy, enabling accurate recognition of the food items. Additionally, our system incorporates calorie estimation algorithms to estimate the calorie content of the recognized food items, providing valuable information for users concerned about their nutritional intake. Overall, our food recognition and calorie estimation system offers an efficient and effective solution for automatically recognizing food items and estimating their calorie content using state-of-the-art deep learning techniques.

Downloads

Download data is not yet available.

References

Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., & Lee, Artificial intelligence in the 21st century. IEEE Access, 6, 34403-34421(2018).

Jiangpeng He, Zeman Shao, Janine Wright, Deborah Kerr, Carol Boushey and Fengqing Zhu, Multi-Task Image-Based Dietary Assessment for FoodRecognition and Portion Size Estimation ,IEEE(2020).

Simon Mezgec, Barbara Korousic Seljak, Using Deep Learning for Food and Beverage Image Recognition, IEEE (2019).

Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou, Stergios Christodoulidis, Zeno Stanga, and Stavroula Mougiakakou, An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients , DOI 10.1109/TMM.2020.2993948, (2020).

Bappaditya Mandal, N. B. Puhan and Avijit Verma, Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data (2018).

LANDU JIANG, BOJIA QIU, XUE LIU, CHENXI HUANG, AND KUNHUI LIN, Deep Food: Food Image Analysis and Dietary Assessment via Deep Model

(2020).

Shuang Ao, Charles X. Lin, Adapting New Categories for Food Recognition with

Deep Representation (2015).

Weishan Zhang, Dehai Zhao, Wenjuan Gong, Zhongwei Li, Qinghua Lu, Su

Yang, Food Image Recognition with Convolutional Neural Networks , UIC-ATC-

ScalCom-CBDCom-IoP (2015).

Raza Yunus, Omar Arif1 , Hammad Afzal, Muhammad Faisal Amjad, Haider

Abbas, Hira Noor Bokhari, Syeda Tazeen Haider1 , Nauman Zafar , And Raheel

Nawaz, A Framework to Estimate the Nutritional Value of Food in Real Time

Using Deep Learning Techniques (2018).

Thai Van Phat, Dang Xuan Tien, Quang Pham, Nguyen Pham, Binh T. Nguyen,

Vietnamese Food Recognition Using Convolutional Neural Networks (2017).

Shuqiang Jiang, Weiqing Min, Linhu Liu and Zhengdong Luo, Multi-Scale Multi-

View Deep Feature Aggregation for Food Recognition (2020).

Mohammed Ahmed Subhi, Sawal Hamid Ali, And Mohammed Abdul Sameer

Mohammed, Vision-Based Approaches for Automatic Food Recognition and

Dietary Assessment: A Survey (2019).

Jabbar, M. A., Abraham, A., Dogan, O., Madureira, A. M., & Tiwari, S. (Eds.).

Deep Learning in Biomedical and Health Informatics: Current Applications and

Possibilities (2021).

Gaurav, D., Shandilya, S., Tiwari, S., & Goyal, A. A machine learning method for

recognizing invasive content in memes. Springer(2020)

Ma, P., Pong Lau, C., Yu, N., Li, A., Liu, P., Wang, Q., Sheng, J., Image-based

Nutrient Estimation for Chinese Dishes Using Deep Learning (2021).

Q. Bojia, Food Recognition and Nutrition Analysis Using Deep CNNs. Montreal,

QC, Canada: McGill Univ., (2019).3552

L. Bossard, M. Guillaumin, and L. Van Gool, Food-101–mining discriminative

components with random forests, Springer, 446–461(2014).

Abadi et al., TensorFlow: A system for large-scale machine learning, pp. 265–

(2016).

V. H. Reddy, S. Kumari, V. Muralidharan, K. Gigoo and B. S. Thakare, Food

Recognition and Calorie Measurement using Image Processing and Convolutional

Neural Network, pp. 109-115 (2019).

N. Tammachat and N. Pantuwong, Calories analysis of food intake using image

recognition, pp. 1-4(2014).

M. A. Subhi and S. Md. Ali, A Deep Convolutional Neural Network for Food

Detection and Recognition, pp. 284-287(2018).

Natarajan, J. Cyber Secure Man-in-the-Middle Attack Intrusion Detection Using Machine Learning Algorithms. In AI and Big Data’s Potential for Disruptive Innovation (pp. 291-316). IGI Global. (2020)

Morquecho-Campos, P., de Graaf, K., & Boesveldt, S. Smelling our appetite? The influence of food odors on congruent appetite, food preferences and intake. Food Quality and Preference, 85, 103959.(2020)

Pal, S. K., Pramanik, A., Maiti, J., & Mitra, P. (2021). Deep learning in multi-object detection and tracking: state of the art. Applied Intelligence, 1-30. (2021)

Fahira, P. K., Rahmadhani, Z. P., Mursanto, P., Wibisono, A., & Wisesa, H. A. Classical Machine Learning Classification for Javanese Traditional Food Image. In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS) (pp. 1-5). IEEE. (2020)

Grattarola, D., & Alippi, C. Graph Neural Networks in TensorFlow and Keras with Spektral [Application Notes]. IEEE Computational Intelligence Magazine, 16(1), 99-106. (2021)

Talukdar, J., Gupta, S., Rajpura, P. S., & Hegde, R. S. Transfer learning for object detection using state-of-the-art deep neural networks. In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 78-83). IEEE. (2018)

Bakke, A. J., Carney, E. M., Higgins, M. J., Moding, K., Johnson, S. L., & Hayes, J. E. Blending dark green vegetables with fruits in commercially available infant foods makes them taste like fruit. Appetite, 150, 104652.(2020)

Zheng, L., Lawlor, B., Katko, B. J., McGuire, C., Zanteson, J., & Eliasson, V. Image processing and edge detection techniques to quantify shock wave dynamics experiments. Experimental Techniques, 1-13. (2020)

Asante-Okyere, S., Shen, C., Ziggah, Y. Y., Rulegeya, M. M., & Zhu, X. Principal component analysis (PCA) based hybrid models for the accurate estimation of reservoir water saturation. Computers & Geosciences, 145, 104555. (2020)

Huynh-The, T., Hua, C. H., & Kim, D. S. Encoding pose features to images with data augmentation for 3-D action recognition. IEEE Transactions on Industrial Informatics, 16(5), 3100-3111. (2019)

Vo, H. V., Pérez, P., & Ponce, J. Toward unsupervised, multi-object discovery in large-scale image collections. In European Conference on Computer Vision (pp. 779-795). Springer, Cham. (2019)

Grinvald, M., Furrer, F., Novkovic, T., Chung, J. J., Cadena, C., Siegwart, R., & Nieto, J. Volumetric instance-aware semantic mapping and 3D object discovery. IEEE Robotics and Automation Letters, 4(3), 3037-3044. (2019)

Downloads

Published

26.03.2024

How to Cite

Yogeshvari Makwana. (2024). Food Recognition and Calorie Assessment from Images Using Convolutional Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3432–3442. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6041

Issue

Section

Research Article