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.

我们将开发用于高分辨率图像的多分辨率分析的新技术，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，并将导致新的字典学习结构和算法的图像和信号处理一般。然后，这些表示将用于分析，搜索和识别绘画（扫描）集合中的类似对象或特征，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，通过我们将构建的字典学习的扩展学习的特征，以及相关的统计相似性，以及专家提供的用于训练分类器的标签和学习项目之间相似性以匹配专家提供的那些的算法，将使我们能够丰富当前构建这些大型绘画网络的能力，在图像和信号中，模板和模式的自动学习以及它们的统计关系通常在各种各样的应用中是至关重要的，例如自动化对象识别，以及定义图像之间的视觉上有意义的相似性，这是在大型图像数据库中进行搜索所需要的。我们都将开发新的技术，用于自动学习图像的良好模板，这些模板包含自然的不变性，如翻译和缩放，以及利用这些模板分析大型集合图像的新方法，测量它们之间的相似性，并发现和表征它们中的循环模式。这些新颖的技术将应用于Jan Brueghel研究网站上的数据，使学者能够调查和概念化一个非常不同的概念。该团队将绘制一张地图，展示早期现代安特卫普工作室制作的数千幅画作之间的相互联系，而不是创建真正和非真正的绝对类别。这些画是几代人在大师的商店里制作的，从世界著名的（彼得·勃鲁盖尔，鲁本斯）到完全默默无闻的。该网站将描绘不同艺术家如何产生、交换、重用和重组想法，绘制创作和生产的网络，远远超出那些通过档案文件可追溯的网络。

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