CFP: Workshop on Cross-media Analysis for Social Multimedia (CASM2014) with ICME2014

[Akisato Kimura <akisato [at] ieee.org>]

日本データベース学会の皆様，

NTT　CS研　木村です．

下記の通り，ICME2014併設のワークショップを企画致しております．
投稿をご検討いただければ幸いです．よろしくお願い致します．

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Call for Papers

1st International Workshop on Cross-media Analysis from Social
Multimedia (CASM),
in conjunction with ICME2014, July 14, 2014, Chengdu, China.

http://sites.google.com/site/casm2014ws/
http://twitter.com/casm2014/

Submission deadline: March 23, 2014
Author notification: April 9, 2014
Camera-ready submission deadline: April 16

We are entering the age of ubiquitous social multimedia. User-generated
multimedia contents such as blogs, short messages, photos and location
check-ins become so pervasive that it is now feasible to exploit them as
sensors for real-world and web-world objects, events and memes. As such,
new algorithms for analyzing unorganized user-generated contents have
become one of the most active research areas in several research
communities. Also, a recent phenomenon called content curation, such as
Storify and Pinterest is now becoming an emerging trend in social media,
which helps us to discover, analyze and retrieve underlying contexts of
user-generated multimedia contents. However, the ability to deeply
analyze and understand those unorganized contents on a single platform
still remains inadequate, even with state-of-the-art multimedia
processing and machine learning techniques.

From the above background, this workshop focuses on one of emerging but
challenging researches topics, cross-media analysis of user-generated
and unorganized social multimedia contents on multiple platforms. Those
social media contents include rich privileged information such as geo
locations, descriptions, user profiles, network and content diffusion
that enable us to view and investigate one specific object, event or
meme from various aspects. This workshop also serves for recent advances
in harvesting and mining multimedia contents along with various types of
privileged information.

This workshop is devoted to the publications of high quality papers on
innovative technical developments and practical applications related to
cross-media analysis from social media, such as:
- Real-world event detection, localization, tracking and understanding
from social multimedia
- Trend detection, diffusion modeling and anomaly detection of social
multimedia
- Context representation, modeling and understanding of multimedia
contents with multiple privileged information from social media
- Sentiment analysis, preference analysis, and user profiling from
social multimedia
- Content analysis, indexing, retrieval and harvesting of social multimedia
- Multimedia content browsing, visualization and mining over multiple
social media platforms
- Mobile cross-media analysis, summarization, visualization and browsing
- Transfer learning, semi-supervised learning and domain adaptation
across multiple social media resources

Authors are invited to submit a paper (two columns, 6 pages maximum)
according to the guidelines available on the ICME2014 website. Only
electronic submissions will be accepted. This workshop uses double-blind
review process for all the regular submissions. Any papers violating
double-blind regulations will be immediately rejected without reviews.
Simultaneous submission to other conferences in the area of multimedia
and related areas is not permitted. Simultaneous submission to journal
publications of significantly extended versions of the paper is
permitted, as long as the publication date of the journal is after the
workshop date.

Workshop organizers:
Akisato Kimura, NTT Communication Science Laboratories, Japan
Winston Hsu, National Taiwan University, Taiwan
Shin'ichi Satoh, National Institute of Informatics, Japan

Contact organizers at casm2014-org at googlegroups com



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Akisato Kimura, Ph.D
        Recognition Research Group
        Media Information Laboratory
        NTT Communication Science Laboratories
    E-mail: akisato [at] ieee.org
    URL: http://www.brl.ntt.co.jp/people/akisato/

    The effort may not be rewarded.
    But nothing great was ever achieved without effort.