Invariant, Intra-Class Retrieval in Digital Image Databases

许多图像数据库使用图像特征来描述一类对象的总体形状和颜色属性，因此，只能进行“类间”区分（例如，飞机与蝴蝶）。这些特征对摄像机视点和场景光照的偶然变化很敏感，严重限制了查询对象的外观。该项目的目标是设计一套新的图像衍生特征，利用详细的局部图像分析来区分同一类别中外观非常相似的物体（例如，在蝴蝶数据库中对不同种类的蝴蝶进行“类内”区分）。这些新特征捕捉了成像物体的基本特征，对全局环境因素（如视点和照明）和局部配置（如形状变形和关节运动）的偶然变化不敏感。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。特别地，构造了灵活的类模板来实现自动图像分割和数据库对象的编目。不变的图像特征以分层的方式组织，使用图像派生和特定于领域的信息，以有效地修剪不太可能的匹配。采用全局结构模型与局部不变特征相结合的表示策略，实现了对附带关节运动和变形不敏感的识别。因此，新的图像特征具有高度的判别性，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、类内的图像检索，即在类似对象的大型数据库中对图像进行高度精确的识别和检索（例如，从蝴蝶数据库中检索旧大陆燕尾的图像），对查询图像的外观几乎没有限制。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来传播。http://www.cs.ucsb.edu/~yfwang

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