A painting authentication method based on multi-scale spatial-spectral feature fusion and convolutional neural network

Zeng, Zimu; Zhang, Pengchang; Qiu, Shi; Li, Siyuan; Liu, Xuebin

doi:10.1016/j.compeleceng.2024.109315

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Cited by 3 publications

References 21 publications

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Computers and Electrical Engineering

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Armed boundary sabotage: A case study of human malicious behaviors identification with computer vision and explainable reasoning methods

Li,

Song,

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Computers and Electrical Engineering

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Rescaled Hinge Loss Function based Convolutional Neural Network for Identification of Art Forgery by Image Analysis

Sundravadivelu,

Karthika,

Al-Fatlawy

et al. 2024

2024 First International Conference on Software, Systems and Information Technology (SSITCON)

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Machine Learning Models for Artist Classification of Cultural Heritage Sketches

Chirosca,

Rădvan,

Mușat

et al. 2024

Applied Sciences

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Modern computer vision algorithms allow researchers and art historians to search for artist-characteristic contour extraction from sketches, thus providing accurate input for artwork analysis, for possible assignments and classifications, and also for the identification of the specific stylistic features. We approach this challenging task with three machine learning algorithms and evaluate their performance on a small collection of images from five distinct artists. These algorithms aim to find the most appropriate artist for a sketch (or a contour of a sketch), with promising results that have a higher level of confidence (around 92%). Models start from common Faster R-CNN architectures, reinforcement learning, and vector extraction tools. The proposed tool provides a base for future improvements to create a tool that aids artwork evaluators.

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A painting authentication method based on multi-scale spatial-spectral feature fusion and convolutional neural network

Cited by 3 publications

References 21 publications

Armed boundary sabotage: A case study of human malicious behaviors identification with computer vision and explainable reasoning methods

Armed boundary sabotage: A case study of human malicious behaviors identification with computer vision and explainable reasoning methods

Rescaled Hinge Loss Function based Convolutional Neural Network for Identification of Art Forgery by Image Analysis

Machine Learning Models for Artist Classification of Cultural Heritage Sketches

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