Shuichi Shiitani

Similarity-based partial image retrieval system for engineering drawings

Designers of mechanical products frequently refer to engineering drawings which are stored as image data in databases to design a new mechanical product efficiently. Multiple mechanical parts are usually drawn on each engineering drawing. Therefore designers want to find engineering drawings containing parts similar to a query image in the shape of a part drawn on an engineering drawing. In this paper, we propose a novel similarity based partial image retrieval system for engineering drawings. A unique aspect of this system is that a graph representation is utilized to robustly find engineering drawings containing similar parts which are invariant to the size, position, and rotation. We verified the performance for the similarity based partial image retrieval system through experiments using industrial engineering drawings. The results show that the top five similar engineering drawings for every query image are always accurately retrieved by our proposed system. This finding suggests that this system could be useful for the reuse of stored engineering drawings.

MIRACLES: Multimedia Information RetrievAl, CLassification, and Exploration System

This paper describes our multimedia information retrieval system, MIRACLES, for retrieving multimedia content such as web pages, business documents, and movies. MIRACLES creates information units, which are collections of related media items, such as an image and text explaining the image. MIRACLES arranges and displays the information units in 3-D space so that similar information units are located near each other. The user can narrow down the retrieval of the multimedia content by rearranging the information units in the display space. MIRACLES is already in practical use, and is employed in some commercial products and services. We are presently evaluating the use of MIRACLES by actual users.

Shape based 3D model retrieval without query

We describe our shape based 3D model retrieval method that is based on a browsing technique. With this method, users can retrieve the desired 3D model efficiently without a query model. In previous retrieval systems, users should provide a 3D model as a query to the system. Then, the system retrieves similar 3D models and returns them to the user. However, the problem of how to obtain the query model remains. With our method, users can retrieve the desired 3D model by walking through a virtual 3D space without the query model. At first, 3D shape features are extracted from all the 3D models, and 3D models are arranged and classified in the virtual 3D space so that similar 3D models are placed near each other. This allows the user to easily grasp where the 3D models similar to the desired one are located. After approaching the 3D model that is similar to the desired one, users can focus on all the nearby models, which are usually similar to the desired one. So users can find the desired one efficiently. We also developed two functions to make our method more efficient. Firstly, our method needs to render a large number of 3D models at one time quickly, so we developed a high-speed rendering method. Secondly, to make it easier for the user to choose the desired one from many 3D models, we developed a method to make 3D models face the direction from which users can recognize the shape of the 3D models easily. In addition, we present the results of experiments to evaluate the retrieval efficiency, which shows that our method is four times as fast as a retrieval method using a query model.

Efficient video retrieval system using virtual 3D space

We propose a technique for efficient retrieval of video scenes in a large number of digital videos. With the technique, cut frame images are automatically detected from videos, and arranged based on a visual feature in a virtual 3D space so that users can easily catch out the target image. We conducted experiments to confirm the effectiveness of the technique. The results show that retrieval by our technique is about 20 times as fast as retrieval by the fast forward playing mode at three times the normal speed.