EAGER: Recurring Pattern Discovery

相似但视觉上不相同的物体形成了在我们生活的世界中无处不在的重复模式。因此，自动循环模式检测算法可以作为迈向强大的更高级别的机器智能的垫脚石。这种重复模式的识别与计算机视觉特别相关，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，图像检索和无标记数据的语义有意义的组织。这个项目探索了从独立于领域的图像和视频中自动发现重复模式，以强大而灵活地捕捉从任何杂乱背景中出现的各种中级视觉线索。这项工作导致有效和高效的对象发现和现场解释。研究小组开发了一种无监督的方法，用于发现单个或多个图像中的重复模式。关键属性是循环的性质，而不知道什么会循环。不同于以前的特征或对象级的配对匹配为基础的方法，从真实的图像的重复模式发现制定为一个联合的，2维的特征分配优化问题，同时考虑多个对象和多个特征集群。该项目通过出版物和与其他研究人员分享数据来传播成果。本项目的研究有助于在更高的空间维度和时空域中理解和捕获重复模式。除了计算机视觉和计算机图形学之外，许多其他研究领域也可以从这项研究中受益。

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.