Malay Festive Seasons Food Recognition for Calorie Detection Using SVM and ECOC Approach
The idea of adding an auto-recognition feature for Malay Festive Seasons Food based on images is a very challenging task in mage computer vision as it is something new and undiscovered before. However, this recognition is important for Malaysian users to manage calorie intake, especially during Hari Raya, one of Malaysia's biggest festive seasons and most celebrated festivals. As color plays an important role in differentiating the type of food, this research aims to implement Color Feature Extraction Method after performing segmentation techniques during the pre-processing phase, where each color from the images will be extracted individually. Then the result from the Color Feature Extraction Method is used to identify the type of food by using Error-Correcting Output Codes (ECOC) classification, which is part of the Support Vector Machine (SVM) algorithm. The reliability and effectiveness of the classifier are evaluated through system testing, where the total overall percentage of correct recognition performed by the system is 82.5%, according to the correct and wrong recognition obtained. The ability to recognize the food correctly after classifying the image is crucial in this research to accurately perform the calorie estimation, whereby the calorie value will be auto generated after food recognition is performed. Besides, thorough research has been conducted on the calorie value for each type of food using reliable internet resources to ensure users can benefit from the system in the future.
 T. J. Key, K. E. Bradbury, A. Perez-Cornago, R. Sinha, K. K. Tsilidis, and S. Tsugane, “Diet, nutrition, and cancer risk: What do we know and what is the way forward?,” BMJ, p. m511, 2020.
 W. Min, S. Jiang, L. Liu, Y. Rui, and R. Jain, “A survey on Food Computing,” ACM Computing Surveys, vol. 52, no. 5, pp. 1–36, 2020.
 M. A. Subhi, S. H. Ali, and M. A. Mohammed, “Vision-based approaches for automatic food recognition and dietary assessment: A survey,” IEEE Access, vol. 7, pp. 35370–35381, 2019.
 D. Allegra, S. Battiato, A. Ortis, S. Urso, and R. Polosa, “A review on Food Recognition Technology for health applications,” Health Psychology Research, vol. 8, no. 3, 2020.
 S. Sasano, X.-H. Han, and Y.-W. Chen, “Food Recognition by Combined Bags of Color Features and Texture Features,” 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2016.
 A. Pisal, R. Sor, and K. S. Kinage, “Facial Feature Extraction Using Hierarchical MAX(HMAX) method,” 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017.
 D. S. Kalel, P. M. Pisal, and R. P. Bagawade, “Color, Shape and Texture feature extraction for Content Based Image Retrieval System: A Study,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 4, pp. 303–306, Apr. 2016.
 N. Thakur and D. Maheshwari, “A Review of Image Classification Techniques,” International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 11, pp. 1588–1591, Nov. 2017.
 T. Ege and K. Yanai, “Multi-task learning of dish detection and calorie estimation,” Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management, 2018.
 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,” 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2019.
 B. N. Esi Nyarko, W. Bin, J. Zhou, G. K. Agordzo, J. Odoom, and E. Koukoyi, “Comparative analysis of Alexnet, resnet-50, and inception-V3 models on masked face recognition,” 2022 IEEE World AI IoT Congress (AIIoT), 2022.
 G. A. Tahir and C. K. Loo, “A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment,” Healthcare, vol. 9, no. 12, p. 1676, 2021.
 R. Khan, S. Kumar, N. Dhingra, and N. Bhati, “The use of different image recognition techniques in food safety: A study,” Journal of Food Quality, vol. 2021, pp. 1–10, 2021.
 T. Kajdanowicz and P. Kazienko, “Multi-label classification using error correcting output codes,” International Journal of Applied Mathematics and Computer Science, vol. 22, no. 4, pp. 829–840, 2012.