April 2012 Newsletter

Welcome to the April edition of the IEEE-TCMC (Technical Committee on Multimedia Computing) monthly mailing. TCMC membership is officially determined by signing up with the IEEE Computer Society either with your membership or later through:


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This month’s topics include:

The Twelfth International Workshop on Multimedia Data Mining (held in conjunction with KDD’12)

International ACM Workshop on Crowdsourcing for Multimedia (held in conjunction with ACM Multimedia 2012)

IEEE Trans. on Multimedia SI on Music Data Mining


Multimedia Data Mining 2012 – Call for Paper

The Twelfth International Workshop on Multimedia Data Mining (MDMKDD 2012)

August 12, 2012
Beijing, China

Workshop website: http://sites.google.com/site/mdmkdd2012/
Mirrored site: http://mdmkdd2012.idm.pku.edu.cn/

The MDM/KDD 2012 workshop is in conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2012).

Important Dates:
• Submission Due: May 13, 2012 (Sunday)
• Acceptance Notification: June 1, 2012 (Friday)
• Camera-ready Due: June 8, 2012 (Friday)
• Workshop Date: August 12, 2012 (Sunday)

Papers accepted for presentation at the workshop will be published in the workshop proceedings and at the ACM digital library.

Paper submission and reviewing will be handled electronically. Authors should consult the workshop Web site for full details regarding paper preparation and submission guidelines: https://sites.google.com/site/mdmkdd2012/submission-instructions

The paper submission site for the MDM/KDD 2012 workshop is: https://cmt.research.microsoft.com/MDMKDD2012/.

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Workshop Topics:

MDM/KDD 2012 will bring together experts in the analysis of digital media content, multimedia databases, knowledge engineers and domain experts from different applied disciplines with potential in multimedia data mining. A new focus in this edition of the workshop is the emerging multimedia applications on mobile devices. Other major topics of the workshop include but are not limited to the following:

• Emerging technology of data mining for mobile applications.
• Emerging technology of data mining on media rich platforms and location enhanced environments (location based services like Foursquare, mobile maps, navigation systems, GIS applications).
• Multimedia data mining across platforms, including web and mobile devices.
• Mining large datasets of user generated content with a geographical dimension.
• Scalable mobile multimedia computing.
• Scalable mobile visual search.
• Predictive and prescriptive multimedia data modeling.
• Privacy preserving data mining.
• Mining multimedia time series.
• Multi-objective multimedia data mining.
• Anomaly and outlier detection in multimedia databases.
• Merging and integration of mining results from different sources (e.g, ensembles, fusion techniques, etc.).
• Scalable data mining techniques for large-scale multimedia databases.
• Human-computer interfaces for multimedia data mining.
• Topic and event discovery in large multimedia repositories.

Formatting Requirements for Submitted Papers

All submissions must be in PDF format and must not exceed 10MB in size. Papers should be no more than 9 pages total in length. The format is the standard double-column ACM Proceedings Style. Additional information about formatting and style files are available online at: http://www.acm.org/sigs/publications/proceedings-templates Papers that do not meet the formatting requirements will be rejected.

For accepted papers, authors will have the opportunity to revise their papers in response to the reviewers before final submission for publication in the proceedings.

The paper submission site for MDM/KDD 2012 is:

Software demonstrations are welcome. We encourage submissions of ‘greenhouse’ work, which present early stages of cutting-edge research and development.

Papers accepted for presentation at the workshop will be published in the workshop proceedings and at the ACM digital library.

For more information regarding submissions, please visit the following page:

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Workshop Co-Chairs:

Aaron Baughman (aaron.baughman@gmail.com), IBM SME/Research Department
Jiang (John) Gao (gao.new@gmail.com), Nokia USA
Tim Pan (jiayu.pan@gmail.com), Google USA
Fang Chu (gammachu@gmail.com), Google China
Yizhou Wang (wangyz@gmail.com), Peking University


International ACM Workshop on Crowdsourcing for Multimedia held in conjunction with ACM Multimedia 2012, Oct 29 – Nov 2 2012, Nara, Japan


CrowdMM 2012 solicits novel contributions to multimedia research that make use of human intelligence, but also take advantage of human plurality. This workshop especially encourages contributions that propose solutions for the key challenges that face widespread adoption of crowdsourcing paradigms in the multimedia research community. These include: identification of optimal crowd members (e.g., user expertise, worker reliability), providing effective explanations (i.e., good task design), controlling noise and quality in the results, designing incentive structures that do not breed cheating, adversarial environments, gathering necessary background information about crowd members without violating privacy, controlling descriptions of task. Particular emphasis will be put on contributions that successfully combine human and automatic methods in order to address multimedia research challenges.

This workshop encourages theoretical, experimental, and/or methodological developments advancing state-of-the-art knowledge of crowd sourcing techniques for multimedia research. Topics include, but are not limited to the use of crowds, wisdom of crowds, or human computation in multimedia, in the following areas of research:

Creation: content synthesis, authoring, editing, and collaboration, summarization and storytelling
Evaluation: evaluation of multimedia signal processing algorithms, multimedia analysis and retrieval algorithms, or multimedia systems and applications
Retrieval: analysis of user multimedia queries, evaluating multimedia search algorithms and interactive multimedia retrieval
Annotation: generating semantic annotations for multimedia content, collecting large-scale input on user affective reactions
Human factors: designing or evaluating user interfaces for multimedia systems, usability study, multi-modal environment, human recognition and perceptions Novel applications (e.g., human as an element in the loop of computation)
Effective Learning from crowd-annotated or crowd-augmented datasets
Quality assurance and cheat detection
Economics and incentive structures
Programming languages, tools and platforms providing enhanced support Inherent biases, limitations and trade-offs of crowd-centered approaches


CrowdMM 2012 welcomes submissions of full papers, as well as short papers reporting work-in-progress. Full papers must be no longer than 6 pages (inclusive of all figures, references and appendices). Short papers are 2 pages, and will be presented as Posters in an interactive setting. All submissions must be written in English and must be formatted according to the ACM Proceedings style. They must contain no information identifying the author(s) or their organization(s). Reviews will be double-blind. Papers will be judged on their relevance, technical content and correctness, and the clarity of presentation of the research.


Accepted full and short papers will appear in the ACM Multimedia 2012 Workshop Proceedings and in the ACM Digital Library.

The authors of selected distinguished papers will be invited to submit extended versions of their papers with fast-track reviews to the special issue “Crowdsourcing for Multimedia” of IEEE Transactions on Multimedia which will be published in late 2013.


Call For Papers — IEEE Transactions on Multimedia

Special Issue on Music Data Mining


During the last few years there has been a dramatic shift in how music is produced, distributed and consumed. A combination of advances in digital storage, audio compression as well as significant increases in network bandwidth has made digital music distribution a reality. Portable music players, computers and smart phones frequently contain personal collections of thousands of music tracks. Digital stores in which users can purchase music contain millions of tracks that can be easily downloaded.

The research area of music data mining has gradually evolved during this time period in order to address the challenge of effectively accessing and interacting with these increasing large collections of music and associated data such as styles, artists, lyrics and music reviews. The algorithms and systems developed frequently employ sophisticated and advanced data mining and machine learning techniques in their attempt to better capture the frequently elusive relevant music information.

Recent advancements in music listening technologies, in particular, the Internet-based music communities, radio stations and music stores, have introduced several new interesting aspects to the area, such as multimodal analysis of music data, community-based labeling of music, user-generated music tags, and listening pattern analysis. The introduction has made the area an exciting research ground and there is a strong and emergent need to publicize the area in multimedia literature.

Topic Areas

The topics covered are (but not limited to):
Keyword generation from song lyrics
Multi-modal classification and clustering of songs
Knowledge mining from symbolic (such as MIDI) data
Knowledge discovery from biography and discography
Modeling of music listening patterns
Playlist generation
Similarity queries
Classification of genre/style/mood
Music recommendation
Music summarization
Text/web mining for music analysis
Database systems/indexing/query models for music analysis
Metadata collection/analysis

Submission Guidelines

Submissions should be submitted through the IEEE Trans. on Multimedia journal web server

(http://mc.manuscriptcentral.com/tmm-ieee). Papers should be formatted according to the guidelines for authors

(http://www.signalprocessingsociety.org/tmm/tmm-author-info/). During the submission, the authors should indicate that this is a submission for the special issue on “Music Data Mining” (i.e., select the appropriate special issue title under the category “Manuscript Type”). All submissions will undergo a blind peer review by three expert reviewers to ensure a high standard of quality.

Referees will consider originality, significance, technical soundness, clarity of exposition, and relevance to the special issue topics above.

Important Dates

Paper submission due: November 19, 2012
First-round acceptance notification: March 19, 2013
Revision Due: June 19, 2013
Second-round review completed: August 19, 2013
Final manuscript due: October 19, 2013
Tentative Publication date: August 2014

Guest Editors

Tao Li, School of Computer Science, Florida International University, USA, email: taoli@cs.fiu.edu (Lead Guest Editor)
Mitsunori Ogihara, Department of Computer Science, University of Miami, USA.
George Tzanetakis, Department of Computer Science, University of Victoria, Canada.

Please address all correspondences regarding this special issue to the Guest Editors.