Fresh food recognition using feature fusion

Pham, Cuong; Thanh, Thuy Nguyen

doi:10.1109/atc.2014.7043401

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…There has been research conducted [25,26] that suggests that people who self-monitor their energy intake may improve their weight loss. Other research has been conducted that utilise computer vision methods to ascertain food type and to quantity portion size [5,6]. Common food log applications use drop down menus to select food items for documentation, computer vision methods offer an alternative that involves finding nutritional information from an image.…”

Section: Discussion and Future Workmentioning

confidence: 99%

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A semi-automated food voting classification system: Combining user interaction and Support Vector Machines

McAllister

Zheng

Bond

et al. 2015

2015 IEEE International Symposium on Technology and Society (ISTAS)

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Obesity is prevalent worldwide including UK and Ireland, affecting all demographics. Obesity can have a detrimental affect on an individual's health, which can lead to chronic conditions. Different digital interventions have enabled users to photograph food items to be identified using different feature extraction methods. In this research, we proposed a system that allows users to draw a polygon around a food item for segmentation. After segmented, the region is then classified using an automated voting system. Different features will then be extracted from the specified area. Support Vector Machines will be issued for each feature type. This system is a proof-of-concept and is designed to research the effectiveness of employing multiple feature detection algorithms to classify food images. To classify food regions a Bag-of-features (BoFs) approach will be used for each. Speeded Up Robust Features point detection and descriptors was used along with colour spatial features, and also MSER region detection with SURF. Each of these methods will have their own BoF to train an SVM. The aim of this research was to create a voting classification system that utilises each feature detection algorithm to ultimately identify the segmented food region through plurality (or majority) vote. Testing showed that the system achieved 75% accuracy when combining each feature SVM to create a voting system. The system outperforms two of the feature classifiers (SURF and MSER with SURF). LAB colour classifier slightly outperformed the voting mechanism within the developed system. In regards to future work, further development and testing would be completed through increasing the variety of food items used in the training phase and a larger test dataset would also be used.

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Section: Discussion and Future Workmentioning

confidence: 99%

“…Results in [5] research show an 81.55% accuracy result using these methods. In [6], SURF and colour features were used to determine different food types and results show 86% precision and 83% recall. In [7], a food recognition system that utilised an optimised version of bag-of-words was created.…”

Section: Related Workmentioning

confidence: 99%

A semi-automated food voting classification system: Combining user interaction and Support Vector Machines

McAllister

Zheng

Bond

et al. 2015

2015 IEEE International Symposium on Technology and Society (ISTAS)

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“…In the existing literature, numerous methodologies assume that the texture, color and shape of food items are well defined [6], [7], [8], [9]. This may not be true because of the local variations in the method of food preparation, as well as the ingredients used.…”

Section: Introduction and Current Approachesmentioning

confidence: 99%

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

Pandey

Deepthi

Mandal

et al. 2017

IEEE Signal Process. Lett.

106

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In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency. Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter

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Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review

Dalakleidi

Papadelli

Kapolos

et al. 2022

Advances in Nutrition

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Dietary assessment can be crucial for the overall well-being of humans and at least in some instances for the prevention and management of chronic, life-threatening diseases. Recall and manual record keeping methods for food intake monitoring are available, but often inaccurate when applied for a long period of time. On the other hand, automatic record keeping approaches that adopt mobile cameras and computer vision methods seem to simplify the process and can improve current human-centric diet monitoring methods. Here we present an extended critical literature overview of image-based food recognition systems (IBFRS) combining a camera of the user's mobile device with computer vision methods and publicly available food datasets (PAFD). In brief, such systems consist of several phases, such as the segmentation of the food items on the plate, the classification of the food items in a specific food category, and the estimation phase of volume, calories or nutrients of each food item. 159 studies were screened in this systematic review of IBFRS. A detailed overview of the methods adopted in each of the 78 included studies of this systematic review of IBFRS is provided along with their performance on PAFD. Studies that included IBFRS without presenting their performance in at least one of the abovementioned phases were excluded. Among the included studies, 45 (58%) studies adopted deep learning methods and especially Convolutional Neural Networks (CNNs) in at least one phase of the IBFRS with input PAFD. Among the implemented techniques, CNNs outperform all other approaches on the PAFD with a large volume of data, since the richness of these datasets provides adequate training resources for such algorithms. We also present evidence for the benefits of application of IBFRS in professional dietetic practice. Furthermore, challenges related to the IBFRS presented here are also thoroughly discussed along with future directions.

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Fresh food recognition using feature fusion

Cited by 8 publications

References 14 publications

A semi-automated food voting classification system: Combining user interaction and Support Vector Machines

A semi-automated food voting classification system: Combining user interaction and Support Vector Machines

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review

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