Takayuki Baba

Attributed Graph Matching Based Engineering Drawings Retrieval

This paper presents a method for engineering drawings retrieval by their shape appearances. In this method, an engineering drawing is represented by an attributed graph, where each node corresponds to a meaningful primitive extracted from the original drawing image. This representation, which characterizes the primitives as well as their spatial relationships by graph nodes attributes and edges attributes respectively, provides a global vision of the drawings. Thus, the retrieval problem can be formulated as one of attributed graph matching, which is realized by mean field theory in this paper. The effectiveness of this method is verified by experiments.

Shape detection from line drawings with local neighborhood structure

An object detection method from line drawings is presented. The method adopts the local neighborhood structure as the elementary descriptor, which is formed by grouping several nearest neighbor lines/curves around one reference. With this representation, both the appearance and the geometric structure of the line drawing are well described. The detection algorithm is a hypothesis-test scheme. The top k most similar local structures in the drawing are firstly obtained for each local structure of the model, and the transformation parameters are estimated for each of the k candidates, such as object center, scale and rotation factors. By treating each estimation result as a point in the parameter space, a dense region around the ground truth is then formed provided that there exist a model in the drawing. The mean shift method is used to detect the dense regions, and the significant modes are accepted as the occurrence of object instances.

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.

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.

Object-driven image group annotation

In this paper, we propose a three-stage method for annotating image groups in real-world photo image databases: (1) Image databases are automatically divided into several image groups so that most photos in each group were taken in the same scene using the time information associated with each photo; (2) Objects in each image are recognized using multiclass object recognition; (3) Each image group is categorized into a scene using all object labels from (2) in the image group. Our main contribution is to propose a novel method for annotating image groups using all objects recognized from all images in this image group. We train our method on 6,000 objects in 696 images from the LabelMe dataset and verify the effectiveness of our proposed method on real-world photo databases consists of 4 outdoor scenes.

Semi-supervised learning by locally linear embedding in kernel space

Graph based semi-supervised learning methods (SSL) implicitly assume that the intrinsic geometry of the data points can be fully specified by an Euclidean distance based local neighborhood graph, however, this assumption may not always be necessarily true. To overcome this problem, we propose to apply locally linear embedding (LLE) method to characterize the geometric structure of the data points; besides this, the embedding process is performed in the kernel induced feature space rather than the original input space. After embedding, the proposed transductive learning method predicts the labels of the unlabeled data within the regularization framework. Experimental results on image retrieval and pattern recognition verify the performance of the proposed approach.

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.