Yusuke Uehara

3D model retrieval based on 2D slice similarity measurements

We present an approach based on 2D slices for measuring similarity between 3D models. The key idea is to represent the 3D model by a series of slices along certain directions so that the shape-matching problem between 3D models is transformed into similarity measuring between 2D slices. Here, we have to deal with the following problems: selection of cutting directions, cutting methods, and similarity measuring. To solve these problems, some strategies and rules are proposed. Firstly, a maximum normal distribution method is presented to get three ortho-axes that coincide better with human visual perception mechanism. Secondly, a cutting method is given which can be used to get a series of slices composed of a set of closed polygons. Thirdly, on the basis of 3D shape distribution method presented by Robert et al., we develop a 2D shape distribution method to measure the similarity between the 2D slices. Some experiments are given in this paper to show the validity of this method for 3D model retrieval.

A robust method for shape-based 3D model retrieval

Proliferation of 3D models necessitates developing efficient methods for indexing or retrieving the models in a large database. Many previous methods for this purpose defined functions on concentric spheres as approximation of 3D geometry for spherical harmonic transform (SHT). In this paper, we point out that this is not robust as the surface of a model may shift between different shells under perturbation, and multi-layer of surfaces may exist in one shell, making the function definition ambiguous. To solve these problems, we propose a method to characterize 3D shape using delta functions. Then, spherical functions are defined by sampling in the frequency domain of the delta functions for SHT. By doing so, our method can support retrieval with controllable acuity, which benefits wider range of applications and facilitates customization to different users. Experiments have shown that our method is more robust than previous approaches.

An images-based 3d model retrieval approach

This paper presents an images based 3D model retrieval method in which each model is described by six 2D images. The images are generated by three steps: 1) the model is normalized based on the distribution of the surface normal directions; 2) then, the normalized model is uniformly sampled to generate a number of random points; 3) finally, the random points are projected along six directions to create six images, each of which is described by Zernike moment feature. In the comparison of two models, six images of each model are naturally divided into three pairs, and the similarity between two models is calculated by summing up the distances of all corresponding pairs. The effectiveness of our method is verified by comparative experiments. Meanwhile, high matching speed is achieved, e.g., it takes about 3e-5 seconds to compare two models using a computer with Pentium IV 3.00GHz CPU.

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.

SVM-based active feedback in image retrieval using clustering and unlabeled data

In content based image retrieval, relevance feedback has been extensively studied to bridge the gap between low level image features and high level semantic concepts. However, it is still challenged by small sample size problem, since users are usually not so patient to label a large number of training instances. In this paper, two strategies are proposed to tackle this problem: (1) a novel active selection criterion. It takes into consideration both the informative and the representative measures. With this criterion, the diversities of the selected images are increased while their informative powers are kept, thus more information gain can be obtained from the feedback images; and (2) incorporation of unlabeled images within the co-training framework. Unlabeled data partially alleviates the training data scarcity problem, thus can improve the efficiency of SVM active learning. Systematic experimental results verify the superiority of our method over some existing active learning methods.

Owlery: a flexible content management system for “growing metadata” of cultural heritage objects and its educational use in the CEAX project

With the Educational use of Cultural heritage Archives and Cross(X) search (CEAX), we have investigated how to establish a framework for managing various kinds of information on cultural heritage objects and how to utilize them for educational purposes. To achieve this goal, we propose a conceptual framework in this paper called “Growing Metadata” and a flexible content management system called Owlery. Growing Metadata includes not only factual descriptions of objects but also various annotations about the objects, such as metadata for children, course materials prepared by school teachers, classroom reports, etc., and are reusable for search and educational purposes. Owlery is a software platform to create, share, utilize and reuse the Growing Metadata, and in which various metadata and annotations are managed in different levels of authenticity, authorship, and user groups. As a result of the experimental classes for 89 6th-grade children, our framework was found to be efficient and accepted by the content creators, like museum experts, content annotators and shool teachers.

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.

Device parts retrieval from assembly drawings with SVM based active relevance feedback

Content based assembly drawing retrieval is valued highly in many application areas, and it is a common thing to seek assembly drawings from a large collection where a specified device part is contained. Different from object detection techniques, a novel solution is presented in this paper to find the occurrences of target objects. Firstly, all device parts are extracted from assembly drawing images according to their specific characteristics. In later retrieval, these parts are compared with the query image to realize the search task. Furthermore, SVM based relevance feedback is adopted to incrementally improve the retrieval performance, and two strategies are proposed: (1) a novel active selection criterion, which takes into consideration both the informative and the representative measures to obtain more information from the feedback images; (2) incorporation of unlabeled images to alleviate the small sample size problem. The performance of this method is verified by extensive experiments.

Shape similarity based on contour decomposition and correspondence

A shape similarity measure based on contour decomposition and part correspondence is introduced. Shapes are approximated by polygons with split-and-merge technique, and a set of perceptual and easily reconfigurable attributes are designed to model the polygonal curves. To compute our similarity measure, the correspondence of polygonal curves is established by DP technique. Then, the similarity between them is computed and aggregated. To verify the effectiveness of our algorithm, we apply it to shape retrieval in two different datasets, one of marine species shapes and another of mechanical shapes, and compare it with some traditional approaches