Semi-supervised Convolutional Triplet Neural Networks for Assessing Paper Texture Similarity
Semi-supervised Convolutional Triplet Neural Networks for Assessing Paper Texture Similarity
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DOI：
10.1109/ieeeconf51394.2020.9443454
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Leah Lackey;Arick Grootveld;Andrew G. Klein
中科院分区：
文献类型：
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作者：
Leah Lackey;Arick Grootveld;Andrew G. Klein
在图形艺术中使用的纸（包括银明胶纸、喷墨纸和编织纸）的背景下，先前的工作已经研究了纹理相似性的测量以用于对这些纸进行分类。大多数先前的工作是基于经典的图像处理方法，如傅立叶，小波和分形分析。在这项工作中，深度学习的最新进展被用来开发一种用于测量纸张纹理相似性的纹理相似性方法。由于可用的数据集通常缺乏标签，因此使用三重丢失来训练卷积神经网络，以最小化来自同一图像的图块的特征距离，同时最大化从不同图像绘制的图块的特征距离。该方法进行了测试，在以前的作品中考虑的三个纸张纹理图像数据库，结果表明，所提出的方法达到了最先进的性能。
In the context of papers used in the graphic arts, including silver gelatin, inkjet, and wove papers, prior work has studied measures of texture similarity for purposes of classifying such papers. The majority of prior work has been based on classical image processing approaches such as Fourier, wavelet, and fractal analysis. In this work, recent advances in deep learning are used to develop a texture similarity approach for measuring paper texture similarity. Since the available datasets generally lack labels, the convolutional neural network is trained using triplet loss to minimize the feature distance of tiles from the same image while simultaneously maximizing the feature distance of tiles drawn from different images. The approach is tested on three paper texture image databases considered in prior works and the results suggest the proposed approach achieves state-of-the-art performance.