Artist-based painting classification using Markov random fields with convolution neural network

Hua, Kai; Ho, Trang-Thi; Jangtjik, Kevin Alfianto; Chen, Yu-Jen; Yeh, Mei-Chen

doi:10.1007/s11042-019-08547-4

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…CNN not only achieves excellent performance on computer vision tasks such as object detection [ 55 , 56 ], image classification [ 57 ], image generation [ 58 ], tracking task [ 59 ], and face recognition [ 60 ], but also can be used to sequence data [ 61 ]. Some works have achieved great results in COVID-19 detection using CNN architecture on textual data.…”

Section: Approachmentioning

confidence: 99%

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Tran

Huang

2022

Sensors

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Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

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Section: Approachmentioning

confidence: 99%

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Tran

Huang

2022

Sensors

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“…One of the most important concepts related to DL is transfer learning [1,[18][19][20][21][22][23][24][25][26][27][28][29][30]. Popular programming and software development platforms such as Matlab or Python offer a wide range of pre-trained CNN models of different structures and complexity.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Learning Approach to Unsupervised Labeling of Fine Art Paintings

2021

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An automatic classification of fine art images is limited by the scarcity of high-quality labels made by art experts. This study aims to provide meaningful automatic labeling of fine art paintings (machine labeling) without the need for human annotation. A new unsupervised Adversarial Clustering System (ACS) is proposed. The ACS is an adversarial learning approach comprising an unsupervised clustering module generating machine labels and a supervised classification module classifying the data based on the machine labels. Both modules are linked through an optimization algorithm iteratively improving the unsupervised clusters. The objective function driving the improvement consists of the within-cluster sum of squares (WCSS) error and the supervised classification accuracy. The proposed method was tested on three different fine-art datasets, including two sets of paintings previously categorized by art experts and one never categorized collection of Australian Aboriginal paintings. The unsupervised clusters were analyzed using standard unsupervised clustering metrics and a reliability measure between machine and human labeling. The ACS showed higher reliability compared to the classical k-means clustering method. The content analysis of unsupervised clusters indicated grouping based on scene composition, type, and shape of the object, edge sharpness and direction, and color palette.

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“…To avoid high-risk stocks and secure higher profits, a stock trader has only one means of evaluating a company’s performance before purchasing its stock. With the development of technological advances, deep learning—especially convolutional neural networks (CNNs)—has exhibited favorable performance in a range of research fields [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Many researchers have applied deep learning to the question of stock market prediction.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation

Huang

2021

Sensors

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Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock’s historical time series data into a candlestick chart to elucidate patterns in the stock’s movement. Finally, we integrated the stock’s sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.

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Artist-based painting classification using Markov random fields with convolution neural network

Cited by 13 publications

References 31 publications

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Adversarial Learning Approach to Unsupervised Labeling of Fine Art Paintings

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation

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