A Novel Sketch-Based Three-Dimensional Shape Retrieval Method Using Multi-View Convolutional Neural Network

Mao, Dianhui; Hao, Zhihao

doi:10.3390/sym11050703

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…For food transactions safety, a blockchain-based food transaction system has been developed in [ 12 ] to ensure the transparency of the transactions, with the effectiveness of this system also verified in [ 13 ]. For food quality and safety, a blockchain-based visualization method according to [ 14 , 15 ] has been proposed to trace the source of problematic products and observe the flow of related risks [ 16 ]. The data in the blockchain can provide a basis for precise regulations, but there is currently a lack of suitable data analysis methods to maximize the effect of the data recorded in the blockchain.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs

Hao

Wang

Mao

et al. 2021

Foods

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As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers’ reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method.

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Section: Theoretical Backgroundmentioning

confidence: 99%

A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs

Hao

Wang

Mao

et al. 2021

Foods

Self Cite

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“…The heat map will show different results over time. In order to measure this, we built a 3D model similar to [39,40]. Figure 7 shows the results of the heat map changes from June to December 2016.…”

Section: An Example Of Using Aquatic Products Datamentioning

confidence: 99%

A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain

Hao

Mao

Zhang

et al. 2020

IJERPH

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Current food traceability systems have a number of problems, such as data being easily tampered with and a lack of effective methods to intuitively analyze the causes of risks. Therefore, a novel method has been proposed that combines blockchain technology with visualization technology, which uses Hyperledger to build an information storage platform. Features such as distribution and tamper-resistance can guarantee the authenticity and validity of data. A data structure model is designed to implement the data storage of the blockchain. The food safety risks of unqualified detection data can be quantitatively analyzed, and a food safety risk assessment model is established according to failure rate and qualification deviation. Risk analysis used visual techniques, such as heat maps, to show the areas where unqualified products appeared, with a migration map and a force-directed graph used to trace these products. Moreover, the food sampling data were used as the experimental data set to test the validity of the method. Instead of difficult-to-understand and highly specialized food data sets, such as elements in food, food sampling data for the entire year of 2016 was used to analyze the risks of food incidents. A case study using aquatic products as an example was explored, where the results showed the risks intuitively. Furthermore, by analyzing the reasons and traceability processes effectively, it can be proven that the proposed method provides a basis to formulate a regulatory strategy for regions with risks.

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“…Recently, deep neural networks (DNNs) are utilized for various kind of problems, which have shown immense performance in different applications, including image recognition [16,40,41]. Deep networks have changed the trend by replacing hand-engineered features to the learning strategy.…”

Section: B Deep Featuresmentioning

confidence: 99%

Deep CNN-based Features for Hand-Drawn Sketch Recognition via Transfer Learning Approach

Hayat¹,

She²,

Mateen³

et al. 2019

IJACSA

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Image-based object recognition is a well-studied topic in the field of computer vision. Features extraction for hand-drawn sketch recognition and retrieval become increasingly popular among the computer vision researchers. Increasing use of touchscreens and portable devices raised the challenge for computer vision community to access the sketches more efficiently and effectively. In this article, a novel deep convolutional neural network-based (DCNN) framework for hand-drawn sketch recognition, which is composed of three wellknown pre-trained DCNN architectures in the context of transfer learning with global average pooling (GAP) strategy is proposed. First, an augmented-variants of natural images was generated and sum-up with TU-Berlin sketch images to all its corresponding 250 sketch object categories. Second, the features maps were extracted by three asymmetry DCNN architectures namely, Visual Geometric Group Network (VGGNet), Residual Networks (ResNet) and Inception-v3 from input images. Finally, the distinct features maps were concatenated and the features reductions were carried out under GAP layer. The resulting feature vector was fed into the softmax classifier for sketch classification results. The performance of proposed framework is comprehensively evaluated on augmented-variants TU-Berlin sketch dataset for sketch classification and retrieval task. Experimental outcomes reveal that the proposed framework brings substantial improvements over the state-of-the-art methods for sketch classification and retrieval.

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A Novel Sketch-Based Three-Dimensional Shape Retrieval Method Using Multi-View Convolutional Neural Network

Cited by 10 publications

References 20 publications

A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs

A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs

A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain

Deep CNN-based Features for Hand-Drawn Sketch Recognition via Transfer Learning Approach

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