Summary of Detecting Korean Food Using Image Using Hierarchical Model, by Hoang Khanh Lam et al.
Detecting Korean Food Using Image using Hierarchical Model
by Hoang Khanh Lam, Kahandakanaththage Maduni Pramuditha Perera
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a system that allows individuals with dietary restrictions to identify Korean dishes by uploading a clear image of the food. The solution leverages image processing techniques and machine learning algorithms to enable users to determine what they are consuming. The proposed approach has the potential to greatly benefit Korean Food lovers who require special diets, enhancing their dining experiences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For people with dietary restrictions, trying new foods can be a challenge. This paper presents a simple solution that uses images of food to help identify dishes. By taking a clear photo of what you’re eating, users can determine if it’s safe for them to consume. The system combines computer vision and machine learning to provide accurate results. |
Keywords
* Artificial intelligence * Machine learning
