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

doi:10.1007/s11042-019-08547-4

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/122966

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题名:	Artist-based painting classification using Markov random fields with convolution neural network
作者:	Hua, Kai-Lung;Ho, Trang-Thi;Jangtjik, Kevin-Alfianto;Yeh, Yu-Jen Chen & Mei-Chen
关键词:	Image classification;Multi-scale pyramid;Markov random fields;Convolutional neural network
日期:	2020-01-21
上传时间:	2023-04-28 16:32:56 (UTC+8)
出版者:	Springer
摘要:	Determining the authorship of a painting image is a challenging task because paintings of an artist may not have a unique style and various artists may have similar painting styles. In this paper, we present a new approach to categorize digital painting images based on artist. We construct a multi-scale pyramid from a painting image to consider both globally and locally the information contained in one image. For each layer, we train a Convolutional Neural Network (CNN) model to determine the class label. To build the relationship within local image patches, we employ Markov random fields (MRFs) by optimizing the Gibbs energy function defined by (1) the data term measuring the compatibility of labeling with given data, and (2) the smoothness term penalizing assignments that label neighboring patches differently. A new fusion scheme is proposed to aggregate patch-level classification results. The proposed CNN-MRF method is validated using two challenging painting image datasets. Experimental results show that the proposed method is effective and achieves state-of-the-art performance.
關聯:	Multimedia Tools and Applications volume 79, p.12635–12658
DOI:	10.1007/s11042-019-08547-4
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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