EAGER: Recurring Pattern Discovery

EAGER：重复模式发现

• 批准号: 1144938
• 负责人: Yanxi Liu
• 金额: $ 15万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144938&HistoricalAwards=false
• 关键词: EAGER; Recurring; Pattern; Discovery

Similar yet visually non-identical objects form recurring patterns that are ubiquitous in the world we live in. Thus an automatic recurring pattern detection algorithm can serve as a stepping stone towards robust higher level machine intelligence. The recognition of such recurring patterns is especially relevant for computer vision since it can lead to saliency detection, image segmentation, image compression and super-resolution, image retrieval and semantically meaningful organization of unlabeled data. This project explores automatic recurring pattern discovery from domain independent images and videos to capture, robustly and flexibly, varying mid-level visual cues emerging from any cluttered background. The work leads to effective and efficient object discovery and scene interpretation. The research team develops an un-supervised method for discovering recurring patterns in a single or multiple images . The key property is the nature of recurring without knowing what recurs. Differing from previous feature- or object-level pairwise-matching-based approaches, recurring pattern discovery from real images is formulated as a joint, 2-dimensional feature assignment optimization problem where multiple objects and multiple feature clusters are considered simultaneously. The project disseminates the results through publications and sharing data with other researchers. The research of this project contributes to the understanding and capturing of recurring patterns in higher spatial dimensions and spatiotemporal domains. Besides computer vision and computer graphics, many other research fields can also benefit from this research.

类似但视觉上的非相同对象形成了我们所生活的世界无处不在的经常性图案。因此，自动重复的图案检测算法可以用作踏板的石材，以实现强大的更高级别的机器智能。这种重复模式的识别与计算机视觉尤其重要，因为它可以导致显着性检测，图像分割，图像压缩和超分辨率，图像检索和语义意义的未标记数据组织。该项目探讨了从域独立图像和视频中自动重复的模式发现，以稳健而灵活地捕获，从任何混乱的背景中出现的中层视觉提示变化。这项工作导致有效，有效的对象发现和场景解释。研究团队开发了一种无监督的方法，用于在单个或多个图像中发现重复的模式。关键特性是不知道重新出现的重复性的本质。与以前的特征级或对象级成对基于基于成对匹配的方法不同，来自真实图像的重复图案发现被公正为关节，二维特征分配优化问题，其中多个对象和多个特征簇同时考虑。该项目通过出版物传播结果并与其他研究人员共享数据。该项目的研究有助于理解和捕获较高空间维度和时空域中的重复模式。除了计算机视觉和计算机图形外，许多其他研究领域也可以从这项研究中受益。