Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we propose to enhance visual representations from neural networks with contextual artistic information. Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period. We design two different approaches for using context in automatic art analysis. In the first one, contextual data is obtained through a multi-task learning model, in which several attributes are trained together to find visual relationships between elements. In the second approach, context is obtained through an art-specific knowledge graph, which encodes relationships between artistic attributes. An exhaustive evaluation of both of our models in several art analysis problems, such as author identification, type classification, or cross-modal retrieval, show that performance is improved by up to 7.3% in art classification and 37.24% in retrieval when context-aware embeddings are used.
翻译:自动艺术分析的目的是通过使用计算机视觉和机器学习技术,对图像收藏中的艺术表现进行分类和检索。在这项工作中,我们提议加强神经网络中带有背景艺术信息的视觉表现。虽然视觉表现能够捕捉到关于艺术作品内容和风格的信息,但我们提议的上下文认知将另外嵌入不同的艺术属性(如作者、学校或历史时期)之间的编码关系。我们设计了两种在自动艺术分析中使用上下文的方法。在第一种情况下,通过多任务学习模型获得背景数据,其中对多个要素进行了培训,以找到各元素之间的视觉关系。在第二种情况下,背景表现是通过一个专门艺术知识图表获得的,该图将艺术属性之间的关系编码起来。对两种模型在诸如作者识别、类型分类或交叉模式检索等若干艺术分析问题中所作的详尽评估表明,艺术分类中的绩效提高了7.3%,在使用背景认知嵌入时的检索中提高了37.24%。