EAGER: Grouping Features for Object Localization and Image Search

EAGER：对象定位和图像搜索的分组功能

• 批准号: 0951754
• 负责人: Song Wang
• 金额: $ 7.5万
• 依托单位: University South Carolina Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0951754&HistoricalAwards=false
• 关键词: EAGER; Grouping; Features; Object; Localization

Numerous images on the internet call for efficient and effective image search algorithms to help users to find images that contain the object of interest. The current state-of-the-art image-search methods usually represent an object of interest as a set of features and localize the object by searching for a rectangular window that covers the desirable features. Without considering spatial relations among the features, these methods usually suffer from a low discriminative power and a high false positive rate. Instead, this EAGER project formulates object localization as a global feature grouping problem, where detected features are grouped according to some general spatial relations, such as group convexity, the separation of boundary and internal features, and feature affinities. The optimal feature grouping is achieved by using new graph models and approaches. Within this formulation, the search window is a tighter bounding polygon rather than a rectangle.By considering spatial relations and tighter bounding polygons, the feature-grouping approach developed in this project is expected to produce a significant improvement over existing image-search methods, which can be verified by testing on a standard data set. The source code developed in this project is planned to be made publicly accessible upon completion of this project. This research is focused on object localization, which can also benefit many other computer-vision applications, such as scene matching and reconstruction, object detection and recognition, content-based video retrieval, and video surveillance.

互联网上大量的图像需要高效的图像搜索算法来帮助用户找到包含感兴趣对象的图像。当前最先进的图像搜索方法通常将感兴趣的对象表示为一组特征，并通过搜索覆盖所需特征的矩形窗口来定位对象。由于没有考虑特征间的空间关系，这些方法的鉴别能力低，误报率高。相反，这个EAGER项目制定了一个全球性的功能分组问题，检测到的功能进行分组，根据一些一般的空间关系，如组凸性，边界和内部功能的分离，和功能的亲和力的对象定位。通过使用新的图模型和方法来实现最佳特征分组。在这个公式中，搜索窗口是一个更紧密的边界多边形，而不是一个rectange.By考虑空间关系和更紧密的边界多边形，在这个项目中开发的特征分组方法，预计将产生显着的改善现有的图像搜索方法，这可以通过测试标准的数据集进行验证。在这个项目中开发的源代码计划在这个项目完成后公开访问。本研究的重点是目标定位，这也可以受益于许多其他计算机视觉应用，如场景匹配和重建，目标检测和识别，基于内容的视频检索，视频监控。