CALL FOR CHAPTERS Proposals Submission Deadline: 6/30/2007 Full Chapters Due: 11/15/2007 Semantic Mining Technologies for Multimedia Databases A book edited by Dr. Dacheng TAO, The Hong Kong Polytechnic University, HK Dr. Dong XU, Columbia University, USA Dr. Xuelong LI, University of London, UK Introduction With the explosive growth of multimedia databases in terms of both size and variety, effective indexing and searching techniques for large-scale multimedia databases have become an urgent research topic in recent years. For data organization, the conventional approach is based on key words or text description of a multimedia datum. However to give all data text annotation is tedious and almost impossible for people to capture. Moreover, the text description is also not enough to precisely describe a multimedia datum. For example, it is unrealistic to utilize words to describe a music clip; an image says more than a thousand words; and keywords-based video shot description cannot characterize the contents for a specific user. Therefore, it is important to utilize the content based approach (CbA) to mine the semantic information of a multimedia datum. The last ten years have witnessed very significant contributions of CbA in semantics targeting for multimedia data organization. CbA means that the data organization, including retrieval and indexing, utilizes the contents of the data themselves, rather than keywords provided by human. Therefore, the contents of a datum could be obtained from techniques in statistics, computer vision, and signal processing. For example, Markov random fields could be applied for image modeling; spatial-temporal analysis is important for video shot representation; and the Mel frequency cepstral coefficient has been shown to be the most effective method for audio signal classification. Apart from the conventional approaches mentioned above, machine learning also plays an indispensable role in current semantic mining tasks, e.g., random sampling techniques and support vector machine for human computer interaction, manifold learning and subspace methods for data visualization, discriminant analysis for feature selection, and classification trees for indexing. The Overall Objective of the Book Recently, multimedia searching and management became very popular because of the demanding applications and the competition among several important companies. It is hot in both academia and industry, while so far, there is no existing book, which covers from basic knowledge to state-of-the-art techniques for multimedia searching and management. The major contributions of this book are: 1) collecting and seeking the recent, most important research results in semantic mining for multimedia data organization, 2) guiding new researchers a comprehensive review on the state-of-the-art techniques for different tasks for multimedia database management, and 3) providing technologists and programmers important algorithms for multimedia system construction. The Target Audience The objective of this book is to provide an introduction to the most recent research techniques in multimedia semantic mining research for new researchers, so that they can go step by step into this field. As a result, they can follow the right way according to their specific applications. The book should serve as an important reference for researchers in multimedia, a handbook for research students, and a repository for multimedia technologists. Recommended topics include, but are not limited to, the following: Part I (Multimedia Data Representation) Global features for image representation Local features for image representation Audio segmentation representation Video shot representation Part II (Human Computer Interaction) Support vector machine base relevance feedback Feature selection in relevance feedback Semi-supervised learning for performance enhancement Active learning for human computer interaction Clustering based relevance feedback Multi-class classification in relevance feedback Part III (Data Visualization) Manifold learning for data visualization Graph techniques for data visualization Intelligent User Interface 3D visualization for data organization Markov random fields for data visualization Part IV (Database Indexing) Point Access Methods, including trees Dynamic indexing structure Dimensionality reduction Semantic classification for indexing Part V (Applications) SUBMISSION PROCEDURE Prospective contributors are invited to submit on or before June 30, 2007, a 2-5 page manuscript proposal clearly explaining the mission and concerns of the proposed chapter(s). Authors of accepted proposals will be notified by July 31, 2007 about the status of their proposals and sent chapter organizational guidelines. Full chapters are expected to be submitted by November 15, 2008. All submitted chapters will be reviewed on a double-blind review basis. The book is scheduled to be published by IGI Global (formerly Idea Group, Inc), http://www.igi-pub.com/, publisher of IGI Publishing (formerly Idea Group Publishing), Information Science Publishing, IRM Press, CyberTech Publishing and Information Science Reference (formerly Idea Group Reference) imprints. Inquiries and submissions can be forwarded electronically (Word document) or by mail to: Dr Dacheng TAO PQ702, 7/F, Building P Department of Computing The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong. Phone: +852 2764-2528 Fax: +852 2764-2528 csdct@comp.polyu.edu.hk (cc: dacheng.tao@gmail.com) Dr Dong XU 1300 S. W. Mudd, 500 West 120th Street, New York, NY 10027, USA. Phone: +1 (212) 854-7477 Fax : +1 (212) 854-7477 dongxu@ee.columbia.edu (cc: dongxudongxu@gmail.com) Dr Xuelong LI Birkbeck, School of Computer Science and Information Systems University of London Malet Street, London WC1E 7HX, U.K. Phone: +44 (20) 7631-6796 Fax: +44 (20) 7631-6727 xuelong_li@ieee.org