Cross-Modal Ingredient Recognition and Recipe Suggestion using Computer Vision and Predictive Modeling
- Title
- Cross-Modal Ingredient Recognition and Recipe Suggestion using Computer Vision and Predictive Modeling
- Creator
- Sanin Zulphi M.; Muzzammil Razin P.A.; Rahul Krishna K.R.; Ramasamy G.
- Description
- This paper is focused on the development of a novel system known as 'IngredEye.' It involves various approaches that can be grouped into categories, such as computer vision, including YOLOv8, a KNN prediction model, and a Flutter framework that hosts all of them in a mobile application environment. Previous studies have analyzed the application of computer vision and OpenCV recognition in cooking and proved that such approaches could enhance the level of convenience in the culinary field. This paper addresses issues like changes in lighting, occlusions, and other factors that have to be solved by the algorithms envisaged for real applications. The objective of this paper solely relies on integrating the OpenCV object detection method with comprehensive machine learning techniques specialized for the culinary field. Presenting the end-user with recipe recommendations based on the visual input they have given. 2024 IEEE.
- Source
- 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Computer vision; Firebase; Flutter; Ingredient recognition; KNN algorithm; Machine learning; Roboflow; YOLOv8
- Coverage
- Sanin Zulphi M., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India; Muzzammil Razin P.A., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India; Rahul Krishna K.R., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India; Ramasamy G., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-833150546-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sanin Zulphi M.; Muzzammil Razin P.A.; Rahul Krishna K.R.; Ramasamy G., “Cross-Modal Ingredient Recognition and Recipe Suggestion using Computer Vision and Predictive Modeling,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/18998.