Invariant, Intra-Class Retrieval in Digital Image Databases

数字图像数据库中的不变类内检索

• 批准号: 9908441
• 负责人: Yuan-Fang Wang
• 金额: $ 21.1万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-10-01 至 2004-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9908441&HistoricalAwards=false
• 关键词: Invariant; Intra; Class; Retrieval; Digital

Many image databases employ image features that describe the aggregate shape and color properties of a class of objects, and hence, are only capable of "inter-classes" discrimination (e.g., airplanes vs. butterflies). These features are sensitive to incidental changes in camera viewpoint and scene illumination, which severely constrain the appearance of query objects. The goal of this project is to design a set of new image-derived features which utilize detailed local image analysis to enable discriminating objects of very similar appearance within the same class (e.g., "intra-class" discrimination of different species of butterflies in a database of butterflies). These new features capture the essential traits of imaged objects in a way that is insensitive to incidental changes in both global environmental factors, such as viewpoint and illumination, and local configuration, such as shape deformation and articulated motion. The project addresses the efficiency and utility issues in deploying these invariant features in large, real-world image databases. In particular, flexible class templates are constructed for automating image segmentation and cataloging of database objects. Invariant image features are organized in a hierarchical manner, using both image-derived and domain-specific information, for efficient pruning of unlikely matches. A representation strategy, which combines global structure models with local invariant features, is used to achieve recognition insensitive to incidental articulated motion and deformation. As a result, the new image features are highly discriminative yet insensitive to incidental changes in shape, viewpoint, and illumination. In particular, they enable invariant, intra-class image retrieval, i.e., highly precise recognition and retrieval of images in large databases of similar objects (e.g., retrieving images of Old World Swallowtails from a database of butterflies) with few constraints on the appearance of query images. The results of the project will be disseminated by scientific papers, software that can be downloaded, and prototype image databases that can be queried on the Web. http://www.cs.ucsb.edu/~yfwang

许多图像数据库采用描述一类对象的集合形状和颜色属性的图像特征，并且因此仅能够进行“类间”区分（例如，飞机对蝴蝶）。这些功能是敏感的相机视点和场景照明，这严重限制了查询对象的外观的偶然变化。该项目的目标是设计一组新的图像衍生特征，这些特征利用详细的局部图像分析来区分同一类中外观非常相似的对象（例如，蝴蝶数据库中不同种类蝴蝶的“类内”区分）。这些新的功能捕捉成像对象的基本特征的方式，是不敏感的全局环境因素，如视点和照明，和本地配置，如形状变形和关节运动的偶然变化。该项目解决了在大型真实图像数据库中部署这些不变特征的效率和实用性问题。特别是，灵活的类模板构造自动图像分割和编目的数据库对象。不变的图像特征以分层的方式组织，使用图像衍生的和特定于域的信息，用于有效地修剪不太可能的匹配。一种表示策略，它结合了全局结构模型与局部不变特征，用于实现识别不敏感的附带关节运动和变形。因此，新的图像特征具有高度的鉴别力，但对形状、视点和照明的偶然变化不敏感。特别是，它们能够实现不变的类内图像检索，即，在类似对象的大型数据库中高度精确地识别和检索图像（例如，从蝴蝶数据库中检索旧世界燕尾蝶的图像），对查询图像的外观几乎没有约束。该项目的成果将通过科学论文、可下载的软件和可在网上查询的原型图像数据库传播。 http://www.cs.ucsb.edu/~yfwang