Passalis Georgios, Kakadiaris Ioannis A, Theoharis Theoharis
Department of Informatics and Telecommunications, University of Athens, Greece.
IEEE Trans Pattern Anal Mach Intell. 2007 Feb;29(2):218-29. doi: 10.1109/TPAMI.2007.37.
As the size of the available collections of 3D objects grows, database transactions become essential for their management with the key operation being retrieval (query). Large collections are also precategorized into classes so that a single class contains objects of the same type (e.g., human faces, cars, four-legged animals). It is shown that general object retrieval methods are inadequate for intraclass retrieval tasks. We advocate that such intraclass problems require a specialized method that can exploit the basic class characteristics in order to achieve higher accuracy. A novel 3D object retrieval method is presented which uses a parameterized annotated model of the shape of the class objects, incorporating its main characteristics. The annotated subdivision-based model is fitted onto objects of the class using a deformable model framework, converted to a geometry image and transformed into the wavelet domain. Object retrieval takes place in the wavelet domain. The method does not require user interaction, achieves high accuracy, is efficient for use with large databases, and is suitable for nonrigid object classes. We apply our method to the face recognition domain, one of the most challenging intraclass retrieval tasks. We used the Face Recognition Grand Challenge v2 database, yielding an average verification rate of 95.2 percent at 10-3 false accept rate. The latest results of our work can be found at http://www.cbl.uh.edu/UR8D/.
随着可用3D对象集合规模的不断扩大，数据库事务对于其管理变得至关重要，其中关键操作是检索（查询）。大型集合也会被预先分类到各个类别中，以便单个类别包含相同类型的对象（例如，人脸、汽车、四足动物）。研究表明，通用对象检索方法不足以完成类内检索任务。我们主张此类类内问题需要一种专门的方法，该方法能够利用基本的类别特征以实现更高的准确性。本文提出了一种新颖的3D对象检索方法，该方法使用类对象形状的参数化带注释模型，并融入其主要特征。基于带注释细分的模型通过可变形模型框架拟合到类的对象上，转换为几何图像并变换到小波域。对象检索在小波域中进行。该方法无需用户交互，具有较高的准确性，在处理大型数据库时效率较高，并且适用于非刚性对象类别。我们将我们的方法应用于人脸识别领域，这是最具挑战性的类内检索任务之一。我们使用了人脸识别大挑战v2数据库，在误识率为10 - 3时，平均验证率达到了95.2%。我们工作的最新结果可在http://www.cbl.uh.edu/UR8D/上找到。
