计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 223-230.doi: 10.11896/jsjkx.211200110
谢秦秦, 何朗, 徐汝利
XIE Qinqin, HE Lang, XU Ruli
摘要: 针对现有油画艺术风格分类算法忽略画面主体区域与整体效果对其艺术风格影响的问题,提出了一种基于多特征融合的油画分类算法(Multi-Feature Fusion Classifier,MFFC)。首先,基于油画艺术元素间常见的排列形式,设计重叠式图像分块法,提取油画空间特征,弥补现有算法中的构图风格缺失,同时区分主体区域与背景区域;其次,将空间特征与底层特征串联融合,增加画面元素的位置信息;最后,设计空间票选法,优先将主体区域的分类结果作为算法结果输出,进一步突出油画主体区域在分类中的作用,实现油画艺术风格的自动分类。在FS-Classifier模型创建的数据集上对所提算法进行测试,其准确率、精确率、召回率、F1-score和AUC分别为96.92%,63.69%,98.75%,98.57%和0.917,相比FS-Classifier分别提升了6.72%,5.85%,9.05%,7.1%和0.128;在公共数据集WIKIART上进行测试,并与其他6种算法进行比较,准确率至少提升了13.27%。实验结果表明,该算法有效提高了空间特征对油画艺术风格分类任务的表现性能,具有良好的实用价值。
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