Where and What Am I Eating? Image-Based Food Menu Recognition

Bolaños, Marc; Valdivia, Marc; Radeva, Petia

doi:10.1007/978-3-030-11024-6_45

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Besides that, In the research conducted by Bolaños, Valdivia, & Radeva (2018), they propose to explore the problem of image-based food menu recognition. The problem here is if given an image, to ascertain the specific menu item corresponding to the restaurant where it was taken to be able to match the image to the menu item, it would be easier to find the exact nutritional information of the food or any other data stored by the restaurant owners.…”

Section: Existing Researchmentioning

confidence: 99%

“…On the other hand, the downside is that it does not take into account the handling of dishes with foreign names which cannot be easily learned by their language model. The conclusion of the study is that it is possible to build a food restaurant menu recognition model for any restaurant, without the need to have a separate model for each restaurant or restaurant pair (Bolaños, Valdivia, & Radeva, 2018).…”

Section: Existing Researchmentioning

confidence: 99%

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Classification of Food Nutrients Composition using Deep Learning

Abdul Aziz,

Habeeb

2019

MyJICT

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Deep Learning is the technique that uses multiple layers of a neural network to automatically distinguish patterns to learn to make predictions. A problem that humans face on a daily basis is how to make a conscious decision regarding our daily food consumption that is nutritious and healthy. By having a tool that helps facilitate the decision making process of what type of food to eat by showing useful nutritional information to us immediately would greatly improve our lives. By critically analysing prominent research papers that relate to deep learning techniques to classify food and their nutrients composition, we decided upon the suitable Deep Learning algorithm to classify food nutrients composition as well as the appropriate image dataset to be used. Therefore in this paper we propose the classification of food nutrients composition utilizing deep learning techniques. The proposed framework uses convolutional neural networks (CNN) as a basis of recognising images of food and classifying the food into their corresponding nutrients composition such as fats, carbohydrates, proteins and more. As part of our future work, we shall use the proposed framework to conduct the training and implementation of the deep learning model to make predictions on food nutrients. The chosen dataset shall be used to train the model where patterns and characteristics of the food images are distinguished over multiple passes of the neural network. Once the model has been trained, then new food images may be introduced to make a prediction from the context that have been learned from before.

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Section: Existing Researchmentioning

confidence: 99%

Section: Existing Researchmentioning

confidence: 99%

Classification of Food Nutrients Composition using Deep Learning

Abdul Aziz,

Habeeb

2019

MyJICT

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“…[Aguilar et al 2018] conducted automatic food tray analysis in canteens and restaurants by food detection and segmentation based on CNNs for smart restaurants. [Bolaños et al 2018] combined CNNs and Recurrent Neural Networks (RNNs) to determine its correct menu item corresponding to the restaurant given an image as the input. Table 3 and Table 4 provides an overview of these approaches with respect to visual features, additional information and recognition type.…”

Section: Mobile Food Recognition the Possibility Of Introducing Smarmentioning

confidence: 99%

A Survey on Food Computing

et al. 2019

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Food is very essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding the human behavior, improving the human health and understanding the culinary culture.With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from disparate sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food related issues in medicine, biology, gastronomy and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, vast amounts of work has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews, which shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this paper, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.

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“…The goal of egocentric vision is to analyze the visual information provided by wearable cameras, which have the capability to acquire images from a first person point-of-view. The analysis of these images provides information about the behavior of the user, useful for several complementary topics like social interactions (Aghaei et al, 2018), scene understanding (Singh et al, 2016), time-space-based localization (Yao et al, 2018), action (Fathi et al, 2011;Possas et al, 2018) or activity recognition (Iwashita et al, 2014;Cartas et al, 2017), or nutritional habits analysis (Bolaños et al, 2018b), among others. Thus, enabling us to understand the whole story and behavior of the users behind the pictures (i.e.…”

Section: Captioning Visual Contentmentioning

confidence: 99%

Interactivity, Adaptation and Multimodality in Neural Sequence-to-sequence Learning

Abril¹

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Chapter 1 frames the scope of this thesis, introducing the pattern recognition field and, more specifically, the MT field. It reviews the different historical approaches devised to tackle this problem. Moreover, it sets the experimental framework followed in this thesis and the main scientific objectives. Chapter 2 describes the mathematical model that represents the core of the thesis: neural networks. It addresses the parameter estimation process, describes different neural architectures and a number of techniques used along the thesis to improve the generalization capability of the model. Chapter 3 introduces the neural machine translation technology, describing the most common architectures and decoding process. Moreover, it reviews different aspects relating the NMT field that nowadays receive the attention of the research community. It also compares NMT in the different translation tasks that will be tackled in the thesis. Chapter 4 introduces the interactive-predictive pattern recognition field, that aims to minimize the effort spent by the user while supervising an automatic system. It proposes the application of this theoretical framework to the neural technology, introducing alternative interaction protocols. After that, these interactivepredictive neural systems are evaluated. Chapter 5 describes the adaptation of NMT systems via online learning techniques. After receiving a corrected sample, the system can be updated to include this new knowledge. Here are described the methods to perform this adaptation and introduces two novel alternatives. In addition, an active learning framework for neural systems is proposed, useful for a situation that requires the translation of large amounts of data. All these scenarios are thoroughly evaluated in a variety of conditions, including a user evaluation involving professional post-editors. Chapter 6 departs from the MT problem to tackle different multimodal sequenceto-sequence tasks. More precisely, it is focused on the generation textual descriptions of videos. These techniques are also applied to the captioning of daily events, captured with an egocentric camera. Finally, the interactive-predictive framework described in Chapter 4 is applied to these multimodal systems. Chapter 7 draws the main conclusions of the thesis, describing the scientific contributions and publications derived from it and traces several lines of future research. These chapters are complemented by two appendices. Appendix A describes NMT-Keras, an open-source library developed to build neural models, that has been used to carry out most of the experiments described in the thesis. In Appendix B we provide the results of a survey carried out in the scope of Chapter 5.

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Where and What Am I Eating? Image-Based Food Menu Recognition

Cited by 6 publications

References 22 publications

Classification of Food Nutrients Composition using Deep Learning

Classification of Food Nutrients Composition using Deep Learning

A Survey on Food Computing

Interactivity, Adaptation and Multimodality in Neural Sequence-to-sequence Learning

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