Learning Personal Preference From Viewer's Operations for Browsing and Its Application to Baseball Video Retrieval and Summarization

Personalization is one of the most important mechanisms to make multimedia systems easy to use. In video applications, its embodiment is to tailor video contents for a particular viewer. For this purpose, we are now developing a system of retrieving and browsing video segments, called video portal with personalization (VIPP). VIPP is characterized by 1) supporting the viewer's access to video contents and making a summarized video clip by taking his/her preference into account and 2) acquiring the viewer's profile from his/her operations automatically. In this paper, we propose a method for learning to personalize from the viewer's operations such as retrieval and browsing, as well as describe how the personalized retrieval and summarization of videos can be realized. From the experiments, we clarify the effect of personalization on retrieval and summarization of baseball videos on VIPP.