课题基金 / 基金详情

Structured Dictionary Models and Learning for High Resolution Images

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:
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
项目状态:
已结题
起止时间:
2016-07-01 至 2018-07-31

项目摘要

项目成果

Mauro Maggioni的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
DOI: --
发表时间: 2016-11
期刊: J. Mach. Learn. Res.
影响因子: --
作者: [Wenjing Liao;M. Maggioni]
通讯作者: Wenjing Liao;M. Maggioni
BIGDATA: F: Compositional Learning, Maps and Transfer: Statistical and Machine Learning on Collections of Data Sets
  • 批准号:
    1837991
  • 项目类别:
    Standard Grant
  • 资助金额:
    $70.0万
  • 财政年份:
    2019
  • 负责人:
    Mauro Maggioni
  • 依托单位:
ATD: Estimation and Anomaly Detection for high-dimensional Data, Maps and Dynamic Processes
  • 批准号:
    1737984
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.0万
  • 财政年份:
    2017
  • 负责人:
    Mauro Maggioni
  • 依托单位:
ATD: Online Multiscale Algorithms for Geometric Density Estimation in High-Dimensions and Persistent Homology of Data for Improved Threat Detection
  • 批准号:
    1756892
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.99万
  • 财政年份:
    2016
  • 负责人:
    Mauro Maggioni
  • 依托单位:
Collaborative Proposal: SI2-CHE: ExTASY Extensible Tools for Advanced Sampling and analYsis
  • 批准号:
    1708353
  • 项目类别:
    Standard Grant
  • 资助金额:
    $14.56万
  • 财政年份:
    2016
  • 负责人:
    Mauro Maggioni
  • 依托单位:
海外基金