With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky and the analysis of the performance of the proposed classifier. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.
翻译:随着大量数字化美术收藏的日益普及,对绘画的自动分析和分类正在成为一个有趣的研究领域,然而,由于领域的特殊性、隐含主观性和普遍存在的细微差别,模糊地将艺术运动分开,利用机器学习技术分析艺术带来了重大挑战。 残余网络或其变体是图像分类任务中最受欢迎的工具之一,可以为定义明确的类别提取相关特征。在本案例研究中,我们侧重于对Johann Kupetzky所选的“绘画家Charles Bruni的肖像”进行分类,并分析拟议的分类师的性能。我们表明,在剩余网络培训中提取的特征可用于在线艺术收藏搜索系统中的图像检索。