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Title: Using deep learning for food recognition
Author: Zhu, Ling
Director/Tutor: Radeva, Petia
Nagarajan, Bhalaji
Keywords: Aprenentatge automàtic
Reconeixement de formes (Informàtica)
Treballs de fi de grau
Visió per ordinador
Xarxes neuronals convolucionals
Machine learning
Pattern recognition systems
Computer software
Computer vision
Bachelor's thesis
Convolutional neural network
Issue Date: 13-Sep-2020
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.
Note: Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Petia Radeva i Bhalaji Nagarajan
Appears in Collections:Programari - Treballs de l'alumnat
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

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