Adaptive Multimedia Retrieval: Identifying, Summarizing, and Recommending Image and Music 6th International Workshop, AMR 2008, Berlin, Germany, June 26-27, 2008. Revised Selected Papers / edited by Marcin Detyniecki, Ulrich Leiner, Andreas Nürnberger.

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Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Detyniecki, Marcin (Editor), Leiner, Ulrich (Editor), Nürnberger, Andreas (Editor)
Format:	eBook
Language:	English
Published:	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
Edition:	1st ed. 2010.
Series:	Information Systems and Applications, incl. Internet/Web, and HCI ; 5811 Springer eBook Collection.
Subjects:	Application software. Electronic resources (E-books)
Online Access:	Click to view e-book
Holy Cross Note:	Loaded electronically. Electronic access restricted to members of the Holy Cross Community.

Table of Contents:

Invited Contribution
The Future of Audio Reproduction
User-Adaptive Web Retrieval
Using Thematic Ontologies for User- and Group-Based Adaptive Personalization in Web Searching
A Poset Based Approach for Condition Weighting
User-Adaptive Music Retrieval
Adaptive User Modeling for Content-Based Music Retrieval
Towards User-Adaptive Structuring and Organization of Music Collections
Music Tracking and Thumbnailing
An Approach to Automatically Tracking Music Preference on Mobile Players
Music Thumbnailing Incorporating Harmony- and Rhythm Structure
Symbolic Music Retrieval
Automatic Reduction of MIDI Files Preserving Relevant Musical Content
Automatic Synchronization between Audio and Partial Music Score Representation
Tagging and Structuring Image Collections
Automatic Image Tagging Using Community-Driven Online Image Databases
Geo-temporal Structuring of a Personal Image Database with Two-Level Variational-Bayes Mixture Estimation
Unsupervised Clustering in Personal Photo Collections
Systems for Still and Motion Images
Towards a Fully MPEG-21 Compliant Adaptation Engine: Complementary Description Tools and Architectural Models
Mobile Museum Guide Based on Fast SIFT Recognition.

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