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

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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.