Artist Identification for Renaissance Paintings
Artist Identification for Renaissance Paintings
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文艺复兴时期绘画的艺术家鉴定
DOI：
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发表时间：
2011
期刊：
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通讯作者：
Sandeep Agrawal
中科院分区：
文献类型：
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作者：
Jonathan Jou;Sandeep Agrawal
目前的作者识别工作主要是针对音乐分类。通过分析艺术家作品的特征来识别艺术家最近引起了人们的兴趣。作为一个多类分类问题，潜在适用的机器学习方法有很多。我们建议扩展目前在这一领域的工作，它使用朴素贝叶斯分类器和多类SVM，通过挑选一组更独特的多产艺术家的画作。我们最初使用颜色直方图作为特征，然后分析更高级的特征，如梯度方向直方图（HOG）。由于与绘画的数量相比，特征的数量很大，我们使用PCA来根据最高方差来调节特征。我们应用了几种多类分类技术，如朴素贝叶斯，线性判别分析，逻辑回归，K-Means和SVM来解决我们的问题，并对5位艺术家的未知画作实现了65%的最大分类准确率。
Current work in author identification is primarily directed towards music classification. Identification of artists by analyzing features of their work has recently gained interest. As a multiclass classification problem, potentially applicable machine learning approaches to the problem are numerous. We propose to extend present work in this area, which uses Naïve Bayes classifiers and multi-class SVMs, by picking a more unique set of paintings across prolific artists. We initially use a histogram of colors as our features, and then we analyze more advanced features like the histogram of gradient orientations (HOG). Due to the large number of features as compared to the number of paintings, we use PCA to condition features based on the highest variance. We apply several multi-class classification techniques like Naïve Bayes, Linear Discriminant Analysis, Logistic Regression, K-Means and SVMs to our problem and achieve a maximum classification accuracy of 65% for an unknown painting across 5 artists.