Large-Scale Specific Object Recognition and Its Application to Real-World Oriented Web

面向现实世界的Web是一个物联网，它扮演着信息的输入/输出标签的角色。与物体相关的信息可以通过使用相机拍摄它的图像来检索。用户可以通过拍摄对象的图像将相关信息与对象相关联。作为要处理的对象，我们考虑各种类型，如字符，文档，3D对象和面孔。在这项研究中，我们开发了一种具有以下能力的对象识别方法，用于实现上述Web：（a）大规模（对于文档，1亿页），快速（对于文档，27ms/查询），准确（对于文档99%）。此外，我们建立了一个理论框架，以及面向现实世界的Web原型。

The real-world oriented Web is a web of things that play a role of input/output tags of information. Information relevant to an object can be retrieved by just taking an image of it by using a camera. The user can associate relevant information to an object by taking its image. As objects to be handled, we consider various types such as characters, documents, 3D objects and faces. In this research we develop a method of object recognition with the following capabilities for realizing the above Web: (a) large-scale (for documents, 100 million pages), fast (for documents, 27ms/query), and accurate (for documents 99%). In addition we build a theoretical framework as well as prototypes of the real-world oriented Web.