| Download | - Will be available here on September 15, 2026
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| DOI | Resolve DOI: https://doi.org/10.1109/CVPRW67362.2025.00046 |
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| Author | Search for: Valdes, Julio J.1ORCID identifier: https://orcid.org/0000-0003-2930-0325; Search for: Liu, Stephie1; Search for: Yang, Shawn1; Search for: Chen, Yuhao; Search for: Wong, Alexander; Search for: Xi, Pengcheng1ORCID identifier: https://orcid.org/0000-0003-3236-5234 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2025), June 11-15, 2025, Nashville, Tennessee, United States |
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| Subject | food degradation; computer vision; fuzzy clustering; unsupervised learning; vision-language models; multimodal analysis; food safety; visual feature extraction |
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| Abstract | Food safety is essential for those who are vulnerable to foodborne illnesses. This study explores food degradation analysis using computer vision techniques combined with unsupervised machine learning. We extract visual features related to shape, texture, and color and apply fuzzy clustering to identify meaningful degradation states, capturing the gradual nature of food decay without rigid class boundaries. Additionally, we extract separate features using a Vision-Language Model (VLM) and integrate them into the clustering analysis. This multimodal approach enables both low-level visual feature analysis and high-level semantic interpretation of food degradation. Our study yields meaningful insights and lays the foundation for future research in food monitoring and safety. |
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| Publication date | 2025-09-15 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 6778f1b7-d70a-4170-9a13-40fa8af2705e |
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| Record created | 2025-09-18 |
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| Record modified | 2025-09-23 |
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