Structured Dictionary Models and Learning for High Resolution Images

我们将开发用于高分辨率图像的多分辨率分析的新技术，以获得新颖有效的信息表示。这些表示将考虑到图像的自然不变性，并将为图像和一般的信号处理带来新的字典学习结构和算法。然后，这些表示将用于分析、搜索和识别绘画收藏（扫描）中的相似对象或特征，特别是巴洛克艺术家Jan Brueghel的大型收藏。图像及其部分之间的距离，我们将构建的字典学习扩展所学习的特征，以及相关的统计相似性，以及专家提供的用于训练分类器和算法的标签，这些标签可以学习物品之间的相似性以匹配专家提供的相似性，这将使我们能够丰富构建这些大型绘画网络的当前能力集。使用更通用的搜索模式更容易地搜索它们，并通过使用降维技术根据不同的度量对它们进行可视化。通常，图像和信号中的模板和模式及其统计关系的自动学习在各种各样的应用程序中都是至关重要的，例如自动对象识别，以及定义图像之间具有视觉意义的相似性，这需要在大型图像数据库中进行搜索。我们将开发用于自动学习好的图像模板的新技术，这些模板包括平移和缩放等自然不变性，以及利用这些模板来分析大型图像集合、测量它们之间的相似性以及发现和表征它们中的循环模式的新方法。这些新颖的技术将被应用到Jan Brueghel研究网站上的数据，这使得学者们可以调查和概念化一个非常不同的关于早期大师画作的概念。该团队将绘制一张地图，显示安特卫普早期现代工坊中制作的数千幅画作之间的相互联系，而不是将真品和赝品绝对分类。这些画是几代人在大师的商店里完成的，从世界著名的（彼得·勃鲁盖尔，鲁本斯）到完全不知名的。该网站将绘制出不同艺术家的创意是如何产生、交换、再利用和重组的图表，绘制出创作和生产的网络，远远超出了通过档案文件可追溯的范围。

We will develop novel techniques for the multi-resolution analysis of high-resolution images, to obtain novel efficient and information representations. These representations will take into account natural invariances in images, and will lead to novel dictionary learning constructions and algorithms for images and in signal processing in general. These representations will then be used to analyze, search, and recognize similar objects or features in collections of (scans of) paintings, in particular a large collection by the baroque artist Jan Brueghel. The distances between images and portions thereof, the features learned by the extensions of dictionary learning we will construct, and the associated statistical similarities, together with labels provided by experts to be used to train classifiers and algorithms that learn similarities among items to match those provided by expert, will enable us to enrich the current set of capabilities in building these large networks of paintings, to search through them more easily and with more general search patterns, and to visualize them according to different metrics by using dimensionality reduction techniques.The automatic learning of templates and patterns, and their statistical relationships, in images and signals in general is crucial in a wide variety of applications, such as automating object recognition, and in defining visually meaningful similarities between images, needed to enable searches in large image databases. We will both develop novel techniques for automatically learning good templates for images, that incorporate natural invariances such as translations and scalings, and novel ways of exploiting these templates for analyzing large collections images, measuring the similarities between then, and finding and characterizing recurrent patterns in them. These novel techniques will be applied to the data on the Jan Brueghel Research site, that allows scholars to investigate and conceptualize a very different notion of old master pictures. Instead of creating absolute categories of genuine and not-genuine, the team will be drawing a map of interconnections between the thousands of paintings produced in the workshops of early modern Antwerp. These pictures were made over several generations, in the shops of masters ranging from world-famous (Pieter Brueghel, Rubens) to utterly obscure. The website will chart how ideas were generated, exchanged, reused and retooled by different artists, mapping networks of creation and production well beyond those traceable through archival documents.