Structured Dictionary Models and Learning for High Resolution Images

高分辨率图像的结构化字典模型和学习

• 批准号: 1724979
• 负责人: Mauro Maggioni
• 金额: $ 10.55万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724979&HistoricalAwards=false
• 关键词: Structured; Dictionary; Models; Learning; Resolution

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.

我们将开发新的技术来对高分辨率图像进行多分辨率分析，以获得新的高效和信息表示。这些表示将考虑图像中的自然不变性，并将导致用于图像和一般信号处理的新的字典学习结构和算法。然后，这些表示将用于分析、搜索和识别画集(扫描)中的相似对象或特征，特别是巴洛克艺术家简·布鲁盖尔的大型收藏。图像及其部分之间的距离、通过我们将构建的词典学习的扩展而学习的特征、以及相关联的统计相似性，以及由专家提供的用于训练分类器和学习项之间的相似性以匹配由专家提供的那些的算法的标签，将使我们能够丰富构建这些大型绘画网络的当前能力集，通过使用降维技术更容易地搜索它们并使用更通用的搜索模式来根据不同的度量来可视化它们。在图像和信号中的模板和图案及其统计关系的自动学习在广泛的应用中是至关重要的，例如自动对象识别，以及在定义图像之间在视觉上有意义的相似性方面，需要能够在大型图像数据库中进行搜索。我们将开发新的技术来自动学习好的图像模板，其中包括自然不变性，如平移和缩放，以及利用这些模板来分析大量集合图像、测量它们之间的相似性以及发现和表征其中的重复模式的新方法。这些新技术将被应用于Jan Brueghel Research网站上的数据，这使得学者们能够调查和概念化一个非常不同的旧大师画的概念。该团队将不再创造真品和非真品的绝对类别，而是绘制一张地图，展示在早期现代安特卫普的作坊中生产的数千幅画之间的相互联系。这些画是几代人在大师的工作室里制作的，从世界著名的皮特·布鲁格尔(Pieter Brueghel，Rubens)到完全默默无闻。该网站将绘制不同艺术家如何产生、交换、重新使用和重组想法的图表，绘制出远远超出档案文件可追溯的创作和生产网络。

项目成果

Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data

• 发表时间: 2016-11
• 期刊: J. Mach. Learn. Res.
• 作者: Wenjing Liao;M. Maggioni