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Recent Studies on Segmentation Techniques for Food Recognition: A Survey

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Abstract

Food has a direct impact on an individual's life and is a significant area for the research community. Computational techniques in food-related computing are performed to address various food-related issues in the field of agronomy, medicine, biology etc. In this paper, we present a comprehensive review of research exclusively on segmentation techniques used for food computing. This paper illustrates the viable segmentation techniques used for food image segmentation. It also provides a comprehensive review of the same. A relevant survey on 66 research papers has been done to provide different food image segmentation techniques. A comparative study among these techniques is also done based on different parameters like type of algorithm, segmentation technique, dataset, and accuracy. Moreover, this paper focuses on research challenges in food recognition. Also a framework has been proposed in this paper to overcome the problem of watershed and OTSU algorithm.

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  1. Jaypee Institute of Information Technology, Noida, India

    Megha Chopra & Archana Purwar

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  1. Megha Chopra
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  2. Archana Purwar
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Correspondence to Megha Chopra.

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Chopra, M., Purwar, A. Recent Studies on Segmentation Techniques for Food Recognition: A Survey. Arch Computat Methods Eng 29, 865–878 (2022). https://doi.org/10.1007/s11831-021-09598-3

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