トップページ＞デジタルアーカイブ技術研究班＞ラック研究室＞PCM2009

2009年12月25日

PCM2009

 PCM2009-Blog-small.jpg

 

This blog is going to talking about the recent conference, PCM 2009. This 2009 IEEE Pacific-Rim Conference on Multimedia was held in Bangkok, Thailand, during December 15-18. The conference emphasized the multimedia technologies and the user behaviors. First, The video analysis and application, including the network technologies that will be a future trend of Youtube and multimedia in hand-held equipments, have been received much attention nowadays. Second, the user behaviors are increasingly beneficial to the industrial sector in the product design. The rest of this Blog will focus on the user behavior in which my research is categorized.
 
The number of papers related to user behaviors found in the book is approximately 17 papers. The proceedings of this conference was published in
Book title: Advances in Multimedia Information Processing - PCM 2009
Series: Lecture Notes in Computer Science 
Subseries: Information Systems and Applications, incl. Internet/Web, and HCI , Vol. 5879 
Muneesawang, P.; Wu, F.; Kumazawa, I.; Roeksabutr, A.; Liao, M.; Tang, X. (Eds.) 
 
One of seventeen papers present a music recommendation system, which provides an automatic and personalized music playing service based on the time parameter and user’s interesting. Its title is Intelligent Music Playlist Recommendation Based on User Daily Behavior and Music Content. The recommendation system used the training set from the user’s music explicitly rating history and the associated time stamps. Then, the widely used techniques such as artificial neural network and decision tree were used as the kernels of the system.
 
Another paper is a recommendation system for campus-living style. Its system name is the CampusGenie system. This system used the recent technology of GPS navigator with WiFi network to provide the active assistance to students for their life in the campus. This paper showed the prototype that fully utilized the campus outdoor context to assist the freshman to adapt to his college life.
 
My paper is A Movement Data Analysis and Synthesis Tool for Museum Visitors’ Behaviors, which analyzes and synthesizes visitors’ behaviors in museums and art galleries by using our defined parameters. A visit time and a observation distance can be calculated by using the proposed functions. The proposed synthesis algorithm is developed and used in classification. Classifying visitor styles is simply implemented by using the average and variance of their stopover time at and distance to all exhibits as shown in this paper.
 
It was a great opportunity that I could attend to this conference by the traveling grant of GCOE program in Ritsumeikan University. I would like to acknowledge my supervisor, Prof. Ruck Thawonmas and the GCOE program. I also would like to credit Prof. Luca Chittaro, whose research inspired me to synthesize the user movement in museums as published in this conference.

Dr. Kingkarn Sookhanaphibarn

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