Segment Anything in Food Images

Saeed S. Alahmari, Michael Gardner, Tawfiq Salem; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3715-3720

This paper introduces a new approach for food image segmentation utilizing the Segment Anything Model (SAM) with the additional refinement achieved through fine-tuning with Low-Rank Adaptation layers (LoRA). The segmentation task involves generating a binary mask for food in RGB images with pixels categorized as background or food. We conduct various experiments to assess and compare the performance of our proposed method with previous approaches. Our findings indicate that our method consistently outperforms other techniques achieving an accuracy of 94.14%. The improved accuracy of our approach highlights its potential for various applications in food image analysis contributing to the advancement of computer vision techniques in the realm of food recognition and segmentation.