This paper proposes the concept of information retrieval for fine arts database system on the fuzzy set theory, especially concerning to sensitive impression and location data. The authors have already reported several important formulations about the data structure and information retrieval models based on the fuzzy set theory for multimedia database. The fuzzy models of the information retrieval are implemented in the fine arts database system, which has the following features: (1) The procedure of information retrieval is formulated in the fuzzy set theory; (2) This database can treat multimedia data such as document data, sensitive impression, location information, and imagedata. (3) It is possible to retrieve the stored data based on sensitive impression and the location data such as "joyful pictures which have a mountain in the center and there is a tree in the right"; (4) Users can input impression words as a retrieval condition, and estimate their grades such as "low," "medium," and "high"; (5) For the result of information retrieval, the satisfaction grade is calculated based on fuzzy retrieval model; and (6) The stored data are about 400 fine arts paintings which are inserted by the textbook of fine arts currently used at the junior high school and high school in Japan. These features of this system give an effects of the fine arts education, and should be useful for information retrieval of fine arts. The results of this study will become increasingly important in connection with development of multimedia technology.

Handwritten character recognition has been increasing its importance and has been expanding its application areas such as office automation, postal service automation, automatic data entry to computers, etc. It is challenging to develop a handwritten character recognition system with high processing speed, high performance, and high portability, because there is a trade-off among them. In current technology, it is difficult to attain high performance and high processing speed at the same time with single algorithms, and therefore, we need to find an efficient way of combination of multiple algorithms. We present an engineering solution to this problem. The system is based on multi-stage strategy as a whole: The first stage is a simple, fast, and reliable recognition algorithm with low substitution-error rate, and data of high quality are recognized in this stage, whereas sloppily written or degraded data are rejected and sent out to the second stage. The second stage is composed of a sophisticated structural pattern classifier and a pattern matching classifier, and these two complementary algorithms run in parallel (multiple expert approach). We demonstrate the performance of the completed system by experiments using real data.

Graph unification is doubtlessly the most expensive process in unification-based grammar parsing since it takes the vast majority of the total parsing time of natural language sentences. A parsing time overload in unification consists in that, in general, no less than 60% of the graph unifications performed actually fail. Thus one way to achieve unification time speed-up is focusing on an efficient, fast way to deal with such unification failures. In this paper, a process, prior to unification itself, capable of filtering or stopping a considerably high percentage of graphs that would fail unification is proposed. This unification-filtering process consists of comparison of signatures that correspond to each one of the graphs to be unified. Unification-filter (hereafter UF) is capable of stopping around 87% of the non-unifiable graphs before unification itself takes place. UF takes significantly less time to detect graphs that do not unify and discard them than it would take to unification to fail the attempt to unify the same graphs. As a result of using UF, unification is performed in an around 71% of the time for the fastest known unification algorithm.

This paper proposes a scheme that offers robust extraction of target images in standard view from input facial images, in order to realize accurate and automatic identification of human faces. A standard view for target images is defined using internal facial features, i.e., the two eyes and the mouth, as steady reference points of the human face. Because reliable detection of such facial features is not an easy task in practice, the proposed scheme is characterized by a combination of two steps: first, all possible regions of facial features are extracted using a color image segmentation algorithm, then the target image is selected from among the candidates defined by tentative combination of the three reference points, through applying the classification framework using the sub-space method. Preliminary experiments on the scheme's flexibility based on subjective assessment indicate a stability of nearly 100% in consistent extraction of target images in the standard view, not only for familiar faces but also for unfamiliar faces, when the input face image roughly matches the front view. By combining this scheme for normalizing images into the standard view with an image matching technique for identification, an experimental system for identifying faces among a limited number of subjects was implemented on a commercial engineering workstation. High success rates achieved in the identification of front view face images obtained under uncontrolled conditions have objectively confirmed the potential of the scheme for accurate extraction of target images.

New methods are needed for accessing very large information services. This paper proposes the use of a user model neural network to allow better access to a news service. The network is constructed on the basis of articles read, and articles marked as rejected. It adapts over time to better represent the user's interests and rank the articles supplied by the news service. Using an augmented keyword search we can also search for articles using keywords in conjunction with the user model neural network. Trials of the system in a USENET news environment show promising results for the use of this approach in information retrieval.

Proposed here is an internal representation and mapping method for multimedia information in which retrieval is based on the impression documents desired to make. A user interface design for a system using this method is also proposed. The proposed internal representation and mapping method represents each desired document impression as an axis in a semantic space. Documents are represented as points in the space. Queries are represented as subspaces. The proposed user interface design employs a method of visual presentation of the semantic space. Pictorial examples are given to illustrate the range of impressions represented by the axes. The relations between the axes are represented by dispersion diagrams for the documents stored in the document base. With this method, the user can intuitively decide the appropriate subspace for his needs and can specify it directly. For evaluation purposes, a prototype system has been developed. An image retrieval experiment shows that the proposed internal representation and mapping method and the user interface design provide effective tools for information retrieval.