In this paper we propose a video retrieval system using texture features from a hand-drawn sketch. There is currently a lot of video content on the Internet due in part to the development of video-sharing Web sites, and sometimes users are unable to narrow the options down to the desired video because there is simply too much content to choose from. We should be able to solve this problem with a retrieval technique that uses internal movie features. Previously, our laboratory proposed a video retrieval system using three internal movie features and a sketch query. In the current study, our aim is to improve the retrieval precision by adding a new feature derived from texture.

Many media forms can be stored easily at present. Photographs, for example, can be easily stored even though most of them have not been edited. This means they will gradually lose their value and become essentially unusable. To make better use of photographs, we tried to make use of information provided by viewers who had seen and commented on them. We felt that analyzing this information would enable us to make maximum use of photographic data. To do this, we defined a ''tag propagation'' model and relationships between photos. We also proposed a system that uses image processing to analyze viewers' handling of photos and how the photos are relevant to each other. We then validated our model by using it.

The recent growth of search technology has enabled people to find information more easily. However, most people need to refind information on a daily basis. Finding and refinding are different activities and require different types of support. However, current refinding support systems don't consider this point. This has caused several problems: PVR, loss of contextual information, and difference in search experiences. We discuss these problems and their solutions from a cognitive perspective. We propose a process-recollective refinding support system based on this discussion. We demonstrate a novel approach to refinding information on the web and a specific system as an example.

A lot of researches that detect the difference of the proficiency are reported for the dynamic scene of sports. Athlete population increases in late years. However, coach same as before population. In this research, it aimed at the helpful information in the beginner’s skill improvement by using the dynamic scene to play badminton for the clearing shot and aimed to acquire it. We compare standard deviation and average time from Ragging-back of stroke in beginner group and expert group to shot. It pretends and it compares it the detection of tracks of the joint part of the racket head to the shot from the Lagging-back beginning. Beginner and expert’s difference and common features are clarified by comparing images of the shot in the there is a shuttle state and the state of pretense. As a result, the feature and the beginner who drew yen while swinging to expert’s tracks got the feature such as gradual seen from the lowest part of tracks to the shot compared with the expert. Moreover, it has been understood that there is a difference between the beginner group and the expert group also at the time that hangs the shot and stability of the shot.