Accuracy improvement of Thai food image recognition using deep convolutional neural networks

Termritthikun, Chakkrit; Kanprachar, Surachet

doi:10.1109/ieecon.2017.8075874

Cited by 19 publications

(16 citation statements)

References 8 publications

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“…A subset of REST dataset containing at least 5 left-hand images (2 images for testing and remaining 3 or more for training per class) of 179 individuals each is used in our work. The datasets comprised with 80 Indian dishes [15] and 50 Thailand dishes [16] are tested. Remaining 4 datasets i.e., the celebrity faces 1 , flowers 2 , marine animals 3 , and skin lesions 4 are collected from the Kaggle repository.…”

Section: Dataset Summarymentioning

confidence: 99%

“…We have experimented on eight small-scale image datasets (1k-15k), representing a wide variations in the object's shape, color, background, texture, etc. The datasets includes human faces with age-variations [11], [12]; hand shapes/palmprint [13], skin lesions [14], natural objects like flowers, underwater sea-lives; and food-dishes of India [15] and Thailand [16]. This paper is an improvement of our earlier published work [17].…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks Fused with Textures for Image Classification

Bera

Bhattacharjee

Nasipuri

2023

Lecture Notes in Networks and Systems

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Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modeling using the long shortterm memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins.

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Section: Dataset Summarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks Fused with Textures for Image Classification

Bera

Bhattacharjee

Nasipuri

2023

Lecture Notes in Networks and Systems

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show abstract

“…Compared with other traditional image feature extraction methods, CNN performs much better. Bossard et al [ 11 ] implemented the CNN model based on the previously proposed network architecture [ 12 , 13 ]. Using images from the Food-101 dataset, an average accuracy rate of 56.4% was obtained in 450,000 iterations.…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework of Visual Checkout System Using Convolutional Neural Networks for Bento Buffet

Lee²,

Hsueh³

2021

Sensors

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In recent years, the technology of artificial intelligence (AI) and robots is rapidly spreading to countries around the world. More and more scholars and industry experts have proposed AI deep learning models and methods to solve human life problems and improve work efficiency. Modern people’s lives are very busy, which led us to investigate whether the demand for Bento buffet cafeterias has gradually increased in Taiwan. However, when eating at a buffet in a cafeteria, people often encounter two problems. The first problem is that customers need to queue up to check out after they have selected and filled their dishes from the buffet. However, it always takes too much time waiting, especially at lunch or dinner time. The second problem is sometimes customers question the charges calculated by cafeteria staff, claiming they are too expensive at the checkout counter. Therefore, it is necessary to develop an AI-enabled checkout system. The AI-enabled self-checkout system will help the Bento buffet cafeterias reduce long lineups without the need to add additional workers. In this paper, we used computer vision and deep-learning technology to design and implement an AI-enabled checkout system for Bento buffet cafeterias. The prototype contains an angle steel shelf, a Kinect camera, a light source, and a desktop computer. Six baseline convolutional neural networks were applied for comparison on food recognition. In our experiments, there were 22 different food categories in a Bento buffet cafeteria employed. Experimental results show that the inception_v4 model can achieve the highest average validation accuracy of 99.11% on food recognition, but it requires the most training and recognition time. AlexNet model achieves a 94.5% accuracy and requires the least training time and recognition time. We propose a hierarchical approach with two stages to achieve good performance in both the recognition accuracy rate and the required training and recognition time. The approach is designed to perform the first step of identification and the second step of recognizing similar food images, respectively. Experimental results show that the proposed approach can achieve a 96.3% accuracy rate on our test dataset and required very little recognition time for input images. In addition, food volumes could be estimated using the depth images captured by the Kinect camera, and a framework of visual checkout system was successfully built.

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“…The use of a large filter to detect objects similarly to the small filter, but the difference is that the Large filter helps to identify or confirm the central position of the object. When the data from the small filter and large filter are concatenated, the model can confirm the position of the desired object as shown in [12,13]. For this reason, the model efficiency has greater accuracy.…”

Section: Nu-litenetmentioning

confidence: 99%

NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks

2019

Self Cite

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The growth of high-performance mobile devices has resulted in more research into on-device image recognition. The research problems are the latency and accuracy of automatic recognition, which remain obstacles to its real-world usage. Although the recently developed deep neural networks can achieve accuracy comparable to that of a human user, some of them still lack the necessary latency. This paper describes the development of the architecture of a new convolutional neural network model, NU-LiteNet. For this, SqueezeNet was developed to reduce the model size to a degree suitable for smartphones. The model size of NU-LiteNet is therefore 2.6 times smaller than that of SqueezeNet. The recognition accuracy of NU-LiteNet also compared favorably with other recently developed deep neural networks, when experiments were conducted on two standard landmark databases.

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Accuracy improvement of Thai food image recognition using deep convolutional neural networks

Cited by 19 publications

References 8 publications

Deep Neural Networks Fused with Textures for Image Classification

Deep Neural Networks Fused with Textures for Image Classification

A Framework of Visual Checkout System Using Convolutional Neural Networks for Bento Buffet

NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks

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