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Comparing Machine Learning vs. Humans for Dietary Assessment by Maryam Abbasi; Cristina Wanzeller; Filipe Cardoso; Pedro Martins is a book available to read on EtoBox.

What is Comparing Machine Learning vs. Humans for Dietary Assessment about?

Due to the availability of large-scale datasets (e.g., Ima-geNet, UECFood) and the advancement of deep Convolutional Neural Networks (CNN), computer vision image recognition has evolved dramatically. Currently, there are three major methods for using CNN: starting from scratch, using a pre-trained network off the shelf, and performing unsupervised pre-training with supervised changes. When it comes to those with dietary restrictions, automatic food detection and assessment are critical. In this research, we show how to address detection difficulties by combining three CNNs. The different CNN architectures are then assessed. The amount of parameters in the examined CNN models ranges from 5,000 to 160 million, depending on the number of layers. Second, the various CNNs under consideration are assessed based on dataset sizes and physical image context. The results are assessed in terms of performance vs. training time vs. accuracy. Finally, the accuracy of CNNs is investigated and examined using human knowledge and classification from the human visual system (HVS). Finally, additional categorization techniques, such as bag-of-words, are considered to solve this problem. Based on the f

Author

Maryam Abbasi; Cristina Wanzeller; Filipe Cardoso; Pedro Martins

Publisher

Springer International Publishing AG

Published

2022

Language

EN

ISBN

9783031148590

Subjects

Engineering, Computer Science, Philosophy