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Document type

Bachelor thesis

Publication date

2020-09-13

Publication license

memòria: cc-nc-nd (c) Ling Zhu, 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/176053

Using deep learning for food recognition

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Abstract

[en] Image recognition is a very challenging and important problem in the computer vision field. And food image classification is one of the most challenging branches of this field. In real-world scenarios, it is more common for a food image to have more than one food item. As a result, the multi-label classification problem has generated significant interest in recent years. However, multi-label recognition is a much more difficult object recognition task compared to single-label recognition. In this work, we will study the multi-label food recognition problem by using deep learning algorithms, specifically Convolutional Neural Networks. We will show how redefining the loss function as well as augmenting the training dataset can leverage the multi-label food recognition problem. Extensive validation will be presented to show the strengths and limitations of multi-label food recognition.

Description

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Petia Radeva i Bhalaji Nagarajan

Subject

Aprenentatge automàtic, Reconeixement de formes (Informàtica), Programari, Treballs de fi de grau, Visió per ordinador, Aliments, Xarxes neuronals convolucionals

Subject (English)

Machine learning, Pattern recognition systems, Computer software, Computer vision, Food, Bachelor's theses, Convolutional neural networks

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Treballs Finals de Grau (TFG) - Enginyeria Informàtica
Programari - Treballs de l'alumnat

Citation

ZHU, Ling. Using deep learning for food recognition. [consulted: 19 of August of 2026]. Available at: https://hdl.handle.net/2445/176053

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