Handbook of Multimedia for Digital Entertainment and Arts- P1

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Handbook of Multimedia for Digital Entertainment and Arts- P1: The advances in computer entertainment, multi-player and online games, technology-enabled art, culture and performance have created a new form of entertainment and art, which attracts and absorbs their participants. The fantastic success of this new field has influenced the development of the new digital entertainment industry and related products and services, which has impacted every aspect of our lives.

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  1. Handbook of Multimedia for Digital Entertainment and Arts
  2. Borko Furht Editor Handbook of Multimedia for Digital Entertainment and Arts 123
  3. Editor Borko Furht Department of Computer Science and Engineering Florida Atlantic University 777 Glades Road PO Box 3091 Boca Raton, FL 33431 USA borko@cse.fau.edu ISBN 978-0-387-89023-4 e-ISBN 978-0-387-89024-1 DOI 10.1007/978-0-387-89024-1 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009926305 c Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
  4. Preface The advances in computer entertainment, multi-player and online games, technology-enabled art, culture and performance have created a new form of enter- tainment and art, which attracts and absorbs their participants. The fantastic success of this new field has influenced the development of the new digital entertainment industry and related products and services, which has impacted every aspect of our lives. This Handbook is carefully edited book – authors are 88 worldwide experts in the field of the new digital and interactive media and their applications in entertain- ment and arts. The scope of the book includes leading edge media technologies and latest research applied to digital entertainment and arts with the focus on interactive and online games, edutainment, e-performance, personal broadcasting, innovative technologies for digital arts, digital visual and auditory media, augmented reality, moving media, and other advanced topics. This Handbook is focused on research issues and gives a wide overview of literature. The Handbook comprises of five parts, which consist of 33 chapters. The first part on Digital Entertainment Technologies includes articles dealing with person- alized movie, television related media, and multimedia content recommendations, digital video quality assessments, various technologies for multi-player games, and collaborative movie annotation. The second part on Digital Auditory Media focuses on articles on digita music management and retrieval, music distribution, music search and recommendation, and automated music video generation. The third part on Digital Visual Media consists of articles on live broadcasts, digital theater, video browsing, projector camera systems, creating believable characters, and other as- pects of visual media. The forth part on Digital Art comprises articles that discuss topics such as infor- mation technology and art, augmented reality and art, creation process in digital art, graphical user interface in art, and new tools for creating arts. The part V on Culture of New Media consists of several articles dealing with interactive narratives, discus- sion on combining digital interactive media, natural interaction in intelligent spaces, and social and interactive applications based on using sound-track identification. With the dramatic growth of interactive digital entertainment and art applica- tions, this Handbook can be the definitive resource for persons working in this field as researchers, scientists, programmers, and engineers. The book is intended for a v
  5. vi Preface wide variety of people including academicians, animators, artists, designers, devel- opers, educators, engineers, game designers, media industry professionals, video producers, directors and writers, photographers and videographers, and researchers and graduate students. This book can also be beneficial for business managers, en- trepreneurs, and investors. The book can have a great potential to be adopted as a textbook in current and new courses on Media Entertainment. The main features of this Handbook can be summarized as: The Handbook describes and evaluates the current state-of-the-art in multimedia technologies applied in digital entertainment and art. It also presents future trends and developments in this explosive field. Contributors to the Handbook are the leading researchers from academia and practitioners from industry. I would like to thank the authors for their contributions. Without their expertise and effort this Handbook would never come to fruition. Springer editors and staff also deserve our sincere recognition for their support throughout the project. Borko Furht Editor-in-Chief Boca Raton, 2009
  6. Contents Part I DIGITAL ENTERTAINMENT TECHNOLOGIES 1 Personalized Movie Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 George Lekakos, Matina Charami, and Petros Caravelas 2 Cross-category Recommendation for Multimedia Content . . . . . . . . . . . . . 27 Naoki Kamimaeda, Tomohiro Tsunoda, and Masaaki Hoshino 3 Semantic-Based Framework for Integration and Personalization of Television Related Media . . . . . . . . . . . . . . . . . . . . . . . . 59 Pieter Bellekens, Lora Aroyo, and Geert-Jan Houben 4 Personalization on a Peer-to-Peer Television System . . . . . . . . . . . . . . . . . . . . 91 Jun Wang, Johan Pouwelse, Jenneke Fokker, Arjen P. de Vries, and Marcel J.T. Reinders 5 A Target Advertisement System Based on TV Viewer’s Profile Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Jeongyeon Lim, Munjo Kim, Bumshik Lee, Munchurl Kim, Heekyung Lee, and Han-kyu Lee 6 Digital Video Quality Assessment Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Anush K. Moorthy, Kalpana Seshadrinathan, and Alan C. Bovik 7 Countermeasures for Time-Cheat Detection in Multiplayer Online Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Stefano Ferretti 8 Zoning Issues and Area of Interest Management in Massively Multiplayer Online Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Dewan Tanvir Ahmed and Shervin Shirmohammadi vii
  7. viii Contents 9 Cross-Modal Approach for Karaoke Artifacts Correction . . . . . . . . . . . . . 197 Wei-Qi Yan and Mohan S. Kankanhalli 10 Dealing Bandwidth to Mobile Clients Using Games . . . . . . . . . . . . . . . . . . . . . 219 Anastasis A. Sofokleous and Marios C. Angelides 11 Hack-proof Synchronization Protocol for Multi-player Online Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Yeung Siu Fung and John C.S. Lui 12 Collaborative Movie Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Damon Daylamani Zad and Harry Agius Part II DIGITAL AUDITORY MEDIA 13 Content Based Digital Music Management and Retrieval . . . . . . . . . . . . . . 291 Jie Zhou and Linxing Xiao 14 Incentive Mechanisms for Mobile Music Distribution . . . . . . . . . . . . . . . . . . 307 Marco Furini and Manuela Montangero 15 Pattern Discovery and Change Detection of Online Music Query Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Hua-Fu Li 16 Music Search and Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Karlheinz Brandenburg, Christian Dittmar, Matthias Gruhne, Jakob Abeßer, Hanna Lukashevich, Peter Dunker, Daniel G¨ rtner, Kay Wolter, Stefanie Nowak, and Holger Grossmann a 17 Automated Music Video Generation Using Multi-level Feature-based Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Jong-Chul Yoon, In-Kwon Lee, and Siwoo Byun Part III DIGITAL VISUAL MEDIA 18 Real-Time Content Filtering for Live Broadcasts in TV Terminals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Yong Man Ro and Sung Ho Jin 19 Digital Theater: Dynamic Theatre Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 Sara Owsley Sood and Athanasios V. Vasilakos 20 Video Browsing on Handheld Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Wolfgang H¨ rst u
  8. Contents ix 21 Projector-Camera Systems in Entertainment and Art . . . . . . . . . . . . . . . . . . 471 Oliver Bimber and Xubo Yang 22 Believable Characters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Magy Seif El-Nasr, Leslie Bishko, Veronica Zammitto, Michael Nixon, Athanasios V. Vasiliakos, and Huaxin Wei 23 Computer Graphics Using Raytracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Graham Sellers and Rastislav Lukac 24 The 3D Human Motion Control Through Refined Video Gesture Annotation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Yohan Jin, Myunghoon Suk, and B. Prabhakaran Part IV DIGITAL ART 25 Information Technology and Art: Concepts and State of the Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 Salah Uddin Ahmed, Cristoforo Camerano, Luigi Fortuna, Mattia Frasca, and Letizia Jaccheri 26 Augmented Reality and Mobile Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Ian Gwilt 27 The Creation Process in Digital Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 Ad´ rito Fernandes Marcos, Pedro S´ rgio Branco, e e and Nelson Troca Zagalo 28 Graphical User Interface in Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 Ian Gwilt 29 Storytelling on the Web 2.0 as a New Means of Creating Arts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 Ralf Klamma, Yiwei Cao, and Matthias Jarke Part V CULTURE OF NEW MEDIA 30 A Study of Interactive Narrative from User’s Perspective . . . . . . . . . . . . . . 653 David Milam, Magy Seif El-Nasr, and Ron Wakkary 31 SoundScapes/Artabilitation – Evolution of a Hybrid Human Performance Concept, Method & Apparatus Where Digital Interactive Media, The Arts, & Entertainment are Combined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 A.L. Brooks
  9. x Contents 32 Natural Interaction in Intelligent Spaces: Designing for Architecture and Entertainment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 Flavia Sparacino 33 Mass Personalization: Social and Interactive Applications Using Sound-Track Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745 Michael Fink, Michele Covell, and Shumeet Baluja Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765
  10. Contributors Jakob Adesser Fraunhofer Institute, Ilmenau, Germany Harry Agius Brunel University, Uxbridge, United Kingdom Dewan Tanvir Ahmed University of Ottawa, Ottawa, Canada Salah Uddin Ahmed Norwegian University of Science and Technology, Norway Marios Angelides Brunel University, Uxbridge, United Kingdom Lora Aroyo Eindhoven University of Technology, Eindhoven, The Netherlands Shumeet Baluja Google, Mountain View, CA, USA Pieter Bellekens Eindhoven University of Technology, Eindhoven, The Netherlands Oliver Bimber Bauhaus University Weimar, Germany Leslie Bishko Simon Fraser University, Vancouver, Canada Alan Bovik University of Texas at Austin, Austin, Texas, USA Karlheinz Bradenburg Fraunhofer Institute, Ilmenau, Germany Pedro Sergio Branco Computer Graphics Center, Guimaraes, Portugal xi
  11. xii Contributors Antony Brooks Aalborg University, Esbjerg, Denmark Siwoo Byun Anyang University, Anyang, Korea Yiwei Cao Technical University of Aachen, Aachen, Germany Cristoforo Camerano University of Catania, Italy Petros Caravelas Athens University of Economics and Business, Athens, Greece Matina Charami Athens University of Economics and Business, Athens, Greece Michele Covell Google, Mountain View, CA, USA Christian Ditmar Fraunhofer Institute, Ilmenau, Germany Peter Dunker Fraunhofer Institute, Ilmenau, Germany Magy Seif El-Naser Simon Fraser University, Vancouver, Canada Stefano Ferretti University of Bologna, Bologna, Italy Michael Fink The Hebrew University of Jerusalem, Israel Jennele Fokker Delft University of Technology, Delft, The Netherlands Luigi Fortuna University of Catania, Italy Mattia Frasca University of Catania, Italy Yeung Siu Fung The Chinese University of Hong Kong, Ma Liu Shui, China Marco Furini University of Modena and Reggio Emilia, Italy Daniel Gartner Fraunhofer Institute, Ilmenau, Germany
  12. Contributors xiii Holger Grossmann Fraunhofer Institute, Ilmenau, Germany Matthias Gruhne Fraunhofer Institute, Ilmenau, Germany Ian Gwilt University of Technology, Sydney, Australia Masaki Hoshino Sony Corporation, Tokyo, Japan Geert-Jan Houben Eindhoven University of Technology, Eindhoven, The Netherlands Wolfgang Huerst Utrecht University, Utrecht, The Netherlands Letizia Jaccheri Norwegian University of Science and Technology, Norway Matthias Jarke Technical University of Aachen, Aachen, Germany Subng Ho Jin Information and Communications University, Deajon, Korea Yohan Jin University of Texas at Dallas, Texas, USA Naoki Kamimaeda Sony Corporation, Tokyo, Japan Mohan Kankanhalli National University of Singapore, Singapore Munchurl Kim Information and Communication University, Daejeon, Korea Munjo Kim Information and Communication University, Daejeon, Korea Ralf Klamma Technical University of Aachen, Aachen, Germany Bumshik Lee Information and Communication University, Daejeon, Korea Han-kyu Lee Electronics and Telecommunications Research Institute, Deajeon, Korea Heekyung Lee Electronics and Telecommunications Research Institute, Deajeon, Korea
  13. xiv Contributors In-Kwoon Lee Yonsei University, Seoul, Korea George Lekakos Athens University of Economics and Business, Athens, Greece Hua-Fu Li Kainan University, Taoyuan, Taiwan Jeongyeon Lim Information and Communication University, Daejeon, Korea John C.S. Lui The Chinese University of Hong Kong, Ma Liu Shui, China Rastislav Lukac Epson Canada Ltd., Toronto, Canada Hanna Lukashevich Fraunhofer Institute, Ilmenau, Germany Aderito Fernnades Marcos University of Minho, Guimaraes, Portugal David Milam Simon Fraser University, Surrey, Canada Manuela Montangero University of Modena and Reggio Emilia, Italy Anush K. Moorthy University of Texas at Austin, Austin, Texas, USA Michael Nixon Simon Fraser University, Vancouver, Canada Stefanie Nowak Fraunhofer Institute, Ilmenau, Germany Johan Pouwelse Delft University of Technology, Delft, The Netherlands B. Prabhakaran University of Texas at Dallas, Texas, USA Marcel Reinders Delft University of Technology, Delft, The Netherlands Yong Man Ro Information and Communication University, Deajon, Korea Graham Sellers Advanced Micro Devices, Orlando, Florida, USA
  14. Contributors xv Kalpana Seshadrinathan University of Texas at Austin, Austin, Texas, USA Shervin Shirmohammadi University of Ottawa, Ottawa, Canada Anastas Sofokleus Brunel University, Uxbridge, United Kingdom Sara Owsley Sood Pomona College, Claremont, CA, USA Flavia Sparacino Sensing Places and MIT, Santa Monica, CA, USA Myunghoon Suk University of Texas at Dallas, Texas, USA Tomohiro Tsunoda Sony Corporation, Tokyo, Japan Arthanasios Vasiliakos University of Peloponnese, Nauplion, Greece Arjen de Vries CWI, Amsterdam, The Netherlands Ron Wakkary Simon Fraser University, Surrey, Canada Jun Wang Delft University of Technology, Delft, The Netherlands Huaxin Wei Simon Fraser University, Vancouver, Canada Kay Wolter Fraunhofer Institute, Ilmenau, Germany Linxing Xiao Tsinghua University, Beijing, China Wei-Qi Yan Queen’s University of Belfast, Belfast, UK Xubo Yang Shanghai Jiao Tong University, Shanghai, China Jong-Chul Yoon Yonsei University, Seoul, Korea Damon Daylamani Zad Brunel University, Uxbridge, United Kingdom
  15. xvi Contributors Nelson Troca Zagalo University of Minho, Braga, Portugal Veronica Zammitto Simon Fraser University, Vancouver, Canada Jie Zhou Tsinghua University, Beijing, China
  16. Part I DIGITAL ENTERTAINMENT TECHNOLOGIES
  17. Chapter 1 Personalized Movie Recommendation George Lekakos, Matina Charami, and Petros Caravelas Introduction The vast amount of information available on the Internet, coupled with the diversity of user information needs, have urged the development of personalized systems that are capable of distinguishing one user from the other in order to provide content, ser- vices and information tailored to individual users. Recommender Systems (RS) form a special category of such personalized systems and aim to predict user’s preferences based on her previous behavior. Recommender systems emerged in the mid-90’s and since they have been used and tested with great success in e-commerce, thus offering a powerful tool to businesses activating in this field by adding extra value to their customers. They have experienced a great success and still continue to efficiently apply on numerous domains such as books, movies, TV program guides, music, news articles and so forth. Tapestry [1], deployed by Xerox PARC, comprises a pioneer implementation in the field of recommender systems and at the same time, it was the first to embed human judgment in the procedure of producing recommendations. Tapestry was an email system capable to manage and distribute electronic documents utilizing the opinion of users that have already read them. Other popular recommender systems that followed are Ringo [2] for music pieces and artists, Last.fm as a personalized internet radio station, Allmusic.com as a metadata database about music genres, similar artists and albums, biographies, reviews, etc, MovieLens [3] and Bellcore [4] for movies, TV3P [5], pEPG [6] and smart EPG [7] as program guides for digital television (DTV), GroupLens [8, 9] for news articles in Usenet and Eigentaste on Jester database as a joke recommender system. Nowadays, Amazon.com [10] is the most popular and successful example of applying recommender systems in order to provide personalized promotions for a plethora of goods such as books, CDs, DVDs, toys, etc. G. Lekakos ( ), M. Charami, and P. Caravelas ELTRUN, the e-Business Center, Department of Management Science and Technology, Athens University of Economics and Business, Athens, Greece e-mail: glekakos@aueb.gr; scha@ait.gr; pcaravel@aueb.gr B. Furht (ed.), Handbook of Multimedia for Digital Entertainment and Arts, 3 DOI 10.1007/978-0-387-89024-1 1, c Springer Science+Business Media, LLC 2009
  18. 4 G. Lekakos et al. Now more than ever, the users continuously face the need to find and choose items of interest among many choices. In order to realize such a task, they usually need help to search and explore or even reduce the available options. Today, there are thousands of websites on the Internet collectively offering an enormous amount of information. Hence, even the easiest task of searching a movie, a song or a restaurant may be transformed to a difficult mission. Towards this direction, search engines and other information retrieval systems return all these items that satisfy a query, usually ranked by a degree of relevance. Thus, the semantics of search engines is characterized by the “matching” between the posted query and the respective re- sults. On the contrary, recommender systems are characterized by features such as “personalized” and “interesting” and hence greatly differentiate themselves form information retrieval systems and search engines. Therefore, recommender systems are intelligent systems that aim to personally guide the potential users inside the underlying field. The most popular recommendation methods are collaborative filtering (CF) and content-based filtering (CBF). Collaborative filtering is based on the assumption that users who with similar taste can serve as recommenders for each other on un- observed items. On the other hand, content-based filtering considers the previous preferences of the user and upon them it predicts her future behavior. Each method has advantages and shortcomings of its own and is best applied in specific situations. Significant research effort has been devoted to hybrid approaches that use elements of both methods to improve performance and overcome weak points. The recent advances in digital television and set-top technology with increased storage and processing capabilities enable the application of recommendation tech- nologies in the television domain. For example products currently promoted through broadcasted advertisements to unknown recipients may be recommended to specific viewers who are most likely to respond positively to these messages. In this way recommendation technologies provide unprecedented opportunities to marketers and suppliers with the benefit of promoting goods and services more effectively while reducing viewers’ advertising clutter caused by the large amount of irrelevant messages [11]. Moreover, the large number of available digital television channels increases the effort required to locate content, such as movies and other programs, that it is most likely to match viewe’s interests. The digital TV vendors do recognize this as a serious problem, and they are now offering personalized electronic program guides (EPGs) to help users navigate this digital maze [12]. This article proposes a movie recommender system, named MoRe, which fol- lows a hybrid approach that combines content-based and collaborative filtering. MoR’s performance is empirically evaluated upon the predictive accuracy of the algorithms as well as other important indicators such as the percentage of items that the system can actually predict (called prediction coverage) and the time required for generating predictions. The remainder of this article is organized as follows. The next section is devoted to the fundamental background of recommender systems describing the main recommendation techniques along with their advantages and limitations. Right after, we illustrate the MoRe system overview and in the section
  19. 1 Personalized Movie Recommendation 5 following, we describe in detail the algorithms implemented. The empirical evalu- ation results are then presented, while the final section provides a discussion about conclusions and future research. Background Theory Recommender Systems As previously mentioned, the objective of recommender systems is to identify which of the information items available are really interesting or likable to individual users. The original idea underlying these systems is based on the observation that people very often rely upon opinions and recommendations from friends, family or asso- ciates to make selections or purchase decisions. Motivated by this “social” approach, recommender systems produce individual recommendations as an output or have the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options [13]. Hence, recommender systems aim at predicting a user’s future behavior based on her previous choices and by relying on features that implicitly or explicitly imply preferences. As shown in Figure 1, the recommendation process usually takes user ratings on observed items and/or item features as input and produces the same output for unobserved items. Many approaches have been designed, implemented and tested on how to process the original input data and produce the final outcome. Still, two of them are the most dominant, successful and widely accepted: collaborative filtering and content-based filtering. Collaborative filtering is the technique that maximally utilizes the “social” aspect of recommender systems, as similar users, called neighbors, are used in order to generate recommendations for the target user. On the other hand, content-based filtering analyses the content of the items according to some features depending on the domain in order to profile the users according to their preferences. These two fundamental approaches are presented in a great detail in the following subsections. Next, we describe some other alternative techniques used in produc- ing personalized recommendations. We continue by realizing comparative obser- vations among all aforementioned techniques, underlying the strengths and the shortcomings of each, thus driving the need of combining them in forming hy- brid recommender systems. Hybrids form the last subsection of the recommender systems background theory. Fig. 1 A high level representation of a recommender system
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