| Abstract |
| For an emotion retrieval system implementation to support pedestrian navigation, coordinating the pedestrian emotion model with the system user`s emotion is considered a key component. This study proposes a new method for capturing the user`s model that corresponds to the pedestrian emotion model and examines the validity of the method. In the first phase, a database comprising a set of interior images that represent hypothetical destinations was developed. In the second phase, 10 subjects were recruited and asked to evaluate on navigation and satisfaction toward each interior image in five rounds of navigation experiments. In the last phase, the subjects` feedback data was used for of the pedestrian emotion model, which is called `learning` in this study. After evaluations by the subjects, the learning effect was analyzed by the following aspects: recall ratio, precision ratio, retrieval ranking, and satisfaction. Findings of the analysis verify that all four aspects significantly were improved after the learning. This study demonstrates the effectiveness of the learning algorithm for the proposed pedestrian emotion model. Furthermore, this study demonstrates the potential of such pedestrian emotion model to be well applicable in the development of various mobile contents service systems dealing with visual images such as commercial interiors in the future. |
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| Key Words |
| Knowledge-Base, Pedestrian Emotion Model, Data Retrieval, Steepest Descent Method, 지식베이스, 보행자 감성모델, 정보탐색, 최대 경사법 |
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