论文标题
绘画数据集中的视觉链接检索和知识发现
Visual link retrieval and knowledge discovery in painting datasets
论文作者
论文摘要
视觉艺术对于我们社会的文化，历史和经济增长而言是不可估量的。视觉艺术中大多数分析的基础之一是在不同艺术家和绘画学校的绘画中找到相似关系。为了帮助艺术史学家更好地了解视觉艺术，本文介绍了一个在数字绘画数据集中进行视觉链接检索和知识发现的框架。通过使用深层卷积神经网络进行特征提取和完全无监督的最近的邻居机制来检索数字化绘画之间的连接，可以实现视觉链路检索。历史知识发现是通过进行图形分析来实现的，该图可以研究艺术家之间的影响。对非常受欢迎的艺术家收集绘画的数据库的实验评估显示了该方法的有效性。无监督的策略使该方法变得有趣，尤其是在元数据稀缺，不可用或难以收集的情况下。
Visual arts are of inestimable importance for the cultural, historic and economic growth of our society. One of the building blocks of most analysis in visual arts is to find similarity relationships among paintings of different artists and painting schools. To help art historians better understand visual arts, this paper presents a framework for visual link retrieval and knowledge discovery in digital painting datasets. Visual link retrieval is accomplished by using a deep convolutional neural network to perform feature extraction and a fully unsupervised nearest neighbor mechanism to retrieve links among digitized paintings. Historical knowledge discovery is achieved by performing a graph analysis that makes it possible to study influences among artists. An experimental evaluation on a database collecting paintings by very popular artists shows the effectiveness of the method. The unsupervised strategy makes the method interesting especially in cases where metadata are scarce, unavailable or difficult to collect.
