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CNN Based Study of Improvised Food Image Classification | IEEE Conference Publication | IEEE Xplore

CNN Based Study of Improvised Food Image Classification


Abstract:

Food recognition has captured numerous research attention for health-related applications. Food recognition is a challenging task due to the diversity of food. Convolutio...Show More

Abstract:

Food recognition has captured numerous research attention for health-related applications. Food recognition is a challenging task due to the diversity of food. Convolutional Neural Networks have addressed the complex feature extraction problem and it has improved the classification accuracy compared to traditional image processing techniques. There are different ways to build the food classification model (i.e.) building CNN from scratch, transfer learning, one shot learning, iterative learning and so on. Transfer learning helps in a better way in generic feature extraction and improves classification accuracy. Along with transfer learning, data augmentation techniques have improved the overall classification summary. In this paper, an automated augmentation technique to remove background objects in food images along with an additional augmentation technique is tried. This has improved the classification accuracy and also partial dataset has been used and compared the classification accuracy for different data set sizes. When the background objects in the food images are removed, CNN trains faster and also provides better performance.
Date of Conference: 08-11 March 2023
Date Added to IEEE Xplore: 18 April 2023
ISBN Information:
Conference Location: Las Vegas, NV, USA

References

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