Food Recognition: A New Dataset, Experiments, and Results

Ciocca, Gianluigi; Napoletano, Paolo; Schettini, Raimondo

doi:10.1109/jbhi.2016.2636441

Cited by 209 publications

(112 citation statements)

References 53 publications

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“…Ciocca and his coauthors contributed two large datasets, Food‐527 (Ciocca, Napoletano, & Schettini, ) and Food‐475 (Ciocca et al., ), and introduced ResNet‐50 model into food image classification, which achieved the best performance on Food‐527, Food‐475, Food‐50, and VIREO. Deep residual networks were once considered as the best structure for common image classification.…”

Section: Deep Learning Applications In Foodmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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Section: Deep Learning Applications In Foodmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

View full text Add to dashboard Cite

show abstract

“…With the popularity of cameras embedded in smartphones and various wearable devices [Vu et al 2017], researchers begin capturing food images in restaurants or canteens for visual food understanding [Ciocca et al 2016;Damen et al 2018;Farinella et al 2014a]. For example, [Ciocca et al 2016] collected food images in a real canteen using the smart phone. Besides food images, [Damen et al 2018] used the head-mounted GoPro to record the cooking videos.…”

Section: Sweetmentioning

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 traditional methods are outperformed by methods using deep learning features or directly using deep learning. [6] uses SVM classifier with CNN features. [21] also uses deep learning features for classification.…”

Section: Related Workmentioning

confidence: 99%

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

Pavlović

2019

Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management

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In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two predictionbased models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database integrative data pre-processing, to solve the problem. Experiments on a dataset of recipes collected from the Internet show the models generate encouraging experimental results. CCS CONCEPTS• Computing methodologies → Neural networks.

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Food Recognition: A New Dataset, Experiments, and Results

Cited by 209 publications

References 53 publications

Application of Deep Learning in Food: A Review

Application of Deep Learning in Food: A Review

A Survey on Food Computing

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

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