Food Image Recognition Using Covariance of Convolutional Layer Feature Maps

Tatsuma, Atsushi; Aono, Masaki

doi:10.1587/transinf.2015edl8212

Cited by 24 publications

(10 citation statements)

References 19 publications

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“…Otherwise, the prediction is wrong. Tatsuma and Aono () reported a new approach for food classification by using the covariances of features of trained CNN as the representation of images, and achieved 58.65% for average accuracy. Yanai and Kawano () used the fine‐tuned AlexNet to achieve the top‐1 accuracy for 70.41%.…”

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

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“…The results of all the state-of-the-art methods are from the leaderboard of SHREC 2016 Large-scale 3D Shape Retrieval from ShapeNet Core55 [1]. In DB-FMCD-FUL-LCDR [44], Feature Maps Covariance Descriptor (FMCD) is calculated on each depth-buffer image rendered for a given 3D shape and ranking scores is calculated by using the Locally Con-strained Diffusion Ranking (LCDR). Then in CCMLT, each 3D shape is rendered into 36 channel of data via concatenation of 36 2D projected images in sequence, where Multi-channel data is utilized to train a feature fusion matrix inside a CNN [45].…”

Section: Comparison To State-of-the-art Methodsmentioning

confidence: 99%

Learning Discriminative 3D Shape Representations by View Discerning Networks

Leng

Zhang

Xiaocheng

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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In view-based 3D shape recognition, extracting discriminative visual representation of 3D shapes from projected images is considered the core problem. Projections with low discriminative ability can adversely influence the final 3D shape representation. Especially under the real situations with background clutter and object occlusion, the adverse effect is even more severe. To resolve this problem, we propose a novel deep neural network, View Discerning Network, which learns to judge the quality of views and adjust their contribution to the representation of shapes. In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors. These score vectors are used to weight the image features and the weighted features performs much better than original features in 3D shape recognition task. In particular, we introduce two structures of Score Generation Unit, Channel-wise Score Unit and Part-wise Score Unit, to assess the quality of feature maps from different perspectives. Our network aggregates features and scores in an end-to-end framework, so that final shape descriptors are directly obtained from its output. Our experiments on ModelNet and ShapeNet Core55 show that View Discerning Network outperforms the state-of-the-arts in terms of the retrieval task, with excellent robustness against background clutter and object occlusion.

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“…After the above processing operations, convolutional layer is used to capture the local features of traffic data. Convolutional layer [33], [34] is the most important part of the CNN, which convolves the input images (or feature maps) with multiple convolutional kernels to create different feature maps. According to [35], the shallower convolutional layers whose receptive field is narrow can extract local information, and while the deeper layers can capture global information with larger vision field.…”

Section: B Multiple Convolutional Layersmentioning

confidence: 99%

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

et al. 2020

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Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The softmax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.

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Food Image Recognition Using Covariance of Convolutional Layer Feature Maps

Cited by 24 publications

References 19 publications

Application of Deep Learning in Food: A Review

Application of Deep Learning in Food: A Review

Learning Discriminative 3D Shape Representations by View Discerning Networks

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

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