May 2011 TCMC Newsletter
Welcome to the May 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:
TCMC home: http://www.computer.org/web/tcmc
This month's topics include:
TCMC meeting and the CFP of IEEE Trans. on Multimedia
The next TCMC meeting is planned to take place in conjunction with
2011 IEEE Intl. Conf. on Multimedia and Expo, July 11-15, Barcelona,
Spain. All TCMC members are welcome to attend.
CALL FOR PAPERS
IEEE Transactions on Multimedia
Special Issue on Learning Semantics from Multimedia Web Resources
Paper submission due: 16-June-2011 (extended to 25 June 2011, firm)
First-round acceptance notification: 18-Sep-2011
Revision Due: 16-Oct-2011
Second-round review completed: 1-Dec-2011
Final manuscript due: 26-Jan-2012
Due to Production date: March 2012
Publication date: June 2012 (expected)
Rapid advances in technology for capturing, processing, distributing,
storing, and presenting visual data has resulted in a proliferation of
multimedia in the World Wide Web. This is reflected in the success of
many social websites, such as Flickr, Youtube, and Facebook, which
dramatically increased the volume of community-shared media, including
images and videos. These websites allow users not only to create
and share media but also to rate and annotate them. Thus significant
amounts of meta-data associated with the media, such as user-provided
tags, comments, geo-tags, capture time, and EXIF information, are
available in the Web. What are needed are methods to organize and
understand these data.
Although the multimedia research community has widely recognized the
importance of learning effective models for organizing and understanding,
it has failed to make rapid progress due to the insufficiency of
labeled data, which typically comes from users in an interactive
labor-intensive manual process. In order to reduce this manual effort,
many semi-supervised learning or active learning approaches have been
proposed. Nevertheless, there is still a need to manually annotate a
large set of images or videos to bootstrap and steer the training. The
rich meta-data associated with the media in the Web offer a way out.
If we can learn the models for semantic concepts effectively from
user-shared media by using their associated meta-data as training labels,
or if we can infer the semantic concepts of the media directly from
the data in the Internet, the manual effort in multimedia annotation
can be reduced. Consequently, semantic-based multimedia retrieval can
greatly benefit from community contributions.
There is, however, a problem in using the associated meta-data as
training labels: they are often very noisy. Thus how to remove the noise
in the training labels or how to handle the noise in the learning
process are important research topics.
Besides modeling media (e.g. images or video), the Web is an incredible
resource for modeling users, through the aggregation of users’ traces
on social media sites (e.g. the images they upload, the tags they
use, the people whose content they comment on). So in addition to
modeling media only, modeling people’s behaviors or events is also
Recently, more and more research effort has been dedicated to the
aforementioned challenges and opportunities. Particularly within the
last year, many papers on this topic have been published in ACM MM,
SIGIR, WWW and CVPR. Therefore, we propose a special issue named
Learning Semantics from Multimedia Web Resources. The goals of this
special issue will be threefold: (1) introduce novel research
in learning from resources in the Internet; (2) survey on the
progress of this area in the past years; (3) discuss new applications
based on the newly learned models.
Topics of interest include (but are not limited to):
* Novel learning methods that learn multimedia semantics from the
web media using the metatext as training labels.
* Regularization strategies to handle the noise in the meta-text
for the learning process.
* Inferring semantics of multimedia data directly from the media
in the Web.
* Web media-based knowledge mining, such as building a
lexicon/ontology from tags, extracting the relations among the
semantic concepts, and learning the similarity metrics.
* Web media analysis and organization, including grouping,
classification, indexing, and navigation.
* Web media tagging, including new tagging interfaces, tag
recommendation, tag classification, tag correction, and automatic
* Training set construction from the multimedia resources in the web.
* Multimedia benchmark dataset creation from the web media, such
as semi-automatic label correction with active learning.
* Social media user and community modeling to improve semantic
relevance of tags.
Submissions should follow the guidelines set out by IEEE Transaction
on Multimedia (http://www.ieee.org/organizations/society/tmm/author_info.html).
Prospective authors should submit high quality, original manuscripts
that have not appeared, nor are under consideration, in any other
journals. Manuscripts should be submitted electronically through
the online IEEE manuscript submission system at
All papers will be reviewed by at least three independent reviewers.
Invited papers will be solicited first through white papers to ensure
the quality and relevance to the special issue. The accepted invited
papers will be reviewed by the guest editors and expect to account
for about one fourth of the papers in the special issue.
Qi Tian, University of Texas at San Antonio, USA, email: firstname.lastname@example.org
Jinhui Tang, National University of Singapore, Singapore, email: email@example.com
Marcel Worring, University of Amsterdam, The Netherlands, email: firstname.lastname@example.org
Daniel Gatica-Perez, Idiap Research Institute, Switzerland, email: email@example.com
Please address all correspondences regarding this special issue to the