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We propose a similarity measuring method for images based on our feature extraction algorithm. The method represents features of an image as a feature vector called reference vector which is a relative measure extracted indirectly from images while many of existing methods use an absolute similarity measure extracted directly from images. A reference vector is calculated from correlation matrices of an image and reference images. Considering reference images as axes of a coordination system, our method enabled users to extract their intended features by selecting appropriate images as reference images. This significant characteristic of the method is effective to measure similarity based on users' preference and to differentiate an image from others. In this paper, we illustrate our method in detail and demonstrate its effectiveness through experiments.

This study aims to make university lectures more enjoyable and fruitful by providing a virtual discussion space closed to outside participants. This web-based educational system has the appearance of an online game, similar to a quiz show. Fearing humiliation, some students hesitate to state their opinions or ask questions in front of unfamiliar classmates. This study tries to resolve this issue by protecting the anonymity of students, thereby providing them with an interactive and cooperative learning environment free from the influence of realworld human relationships, which can significantly influence student engagement. Students can view and compare each other's opinions, answers, response time, and confidence levels, without individual identification. Additionally, I report the results of a questionnaire administered to 107 university students regarding the integration of educational games into my classroom lectures to evaluate the effectiveness of my approach, and present a brief explanation of the educational system I am developing.

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