基于风格的视觉艺术作品聚类

Abhishek Dangeti, Pavan Gajula, Vivek Srivastava, Vikram Jamwal
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引用次数: 0

摘要

基于风格对艺术作品进行聚类有许多潜在的现实应用,如艺术推荐、基于风格的搜索和检索,以及研究艺术作品语料库中艺术风格的演变。然而,基于风格对艺术作品进行聚类在很大程度上是一个尚未解决的问题。现有的几种艺术作品聚类方法主要依赖于深度神经网络生成的通用图像特征表征,并没有专门针对艺术风格进行处理。在本文中,我们介绍并讨论了基于风格的视觉艺术作品聚类概念。我们的主要目标是探索可用于基于风格聚类的神经特征表示和架构,并观察它们的影响和效果。我们开发了不同的方法,并通过定性和定量分析评估了它们在基于风格的聚类中的相对功效,并将其应用于四个艺术作品集和四个经过策划的合成风格数据集。我们的分析为适合基于风格的聚类的架构、特征表示和评估方法提供了一些关键的新见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Style Based Clustering of Visual Artworks
Clustering artworks based on style has many potential real-world applications like art recommendations, style-based search and retrieval, and the study of artistic style evolution in an artwork corpus. However, clustering artworks based on style is largely an unaddressed problem. A few present methods for clustering artworks principally rely on generic image feature representations derived from deep neural networks and do not specifically deal with the artistic style. In this paper, we introduce and deliberate over the notion of style-based clustering of visual artworks. Our main objective is to explore neural feature representations and architectures that can be used for style-based clustering and observe their impact and effectiveness. We develop different methods and assess their relative efficacy for style-based clustering through qualitative and quantitative analysis by applying them to four artwork corpora and four curated synthetically styled datasets. Our analysis provides some key novel insights on architectures, feature representations, and evaluation methods suitable for style-based clustering.
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