Important Dates: Submission due: July 20, 2015 Author notification: September 1, 2015 Workshop date: Nov 14, 2015 |
You are cordially invited to
submit and attend the IEEE International Workshop on Big Media Data (BMD)
2015, in conjunction with IEEE International Conference on Data Mining (ICDM)
2015, November 14-17, Atlantic City, NJ, USA. The explosion of
images, videos and other media data in the Internet, mobile devices, and
desktops has attracted more and more interest in the Big Media research area.
Big media opens great unprecedented opportunities to address many challenging
computing problems, offering a promising possibility for in-depth media
understanding, as well as exploring the very big scale media data to bridge
the well-known semantic gap between high-level semantic and low-level
features. Big media provides richer information, ranging from social
relations to context information associated to rich media data of diverse
modalities. It also provides us the opportunity to mine reliable and helpful
knowledge from Big media for a wide variety of applications. Big media is big in terms of
various dimensions, such as the number of media items, the dimension of the
representation, and the number of concepts, and thus entails a lot of
research challenges and opportunities. For example, how does the traditional
machine learning algorithms, which have been proven efficient and effective
in thousands of data points, scale up to the web-scale big media data with
millions and even billions of items? Seeking the answer motivates us to
design parallel and distributed machine learning platforms, exploiting GPUs
as well as developing practical algorithms to fit in restricted storage
limits and accelerate the algorithms with the ever-growing size of the
database and the dimension. Moreover, how is the big media data organized and
how can it be managed to enable efficient browsing and retrieval? The
research interests in this direction produced many hashing, indexing and
clustering algorithms for high-dimensional data. Besides, it is also
important to construct benchmark data to facilitate and validate the
newly-developed big-media algorithms. This workshop targets the
researchers and practitioners from both the industry and the academia, and provides
a forum to publish recent state-of-the-art achievements in the Big Media
research area. Topics of interest include, but are not limited to: ·
Image
annotation and classification with Big Image Data ·
Video
understanding with Big Video Data ·
Machine
learning platform for Big Media Data ·
Machine
learning algorithms with practical optimization algorithms for Big Data ·
Large
scale clustering for Big Media Data ·
Large
scale neighborhood graph construction for Big Media Data ·
Browsing
and Summarizing the Big Media Data ·
Hashing
algorithms for Big Media Data ·
Indexing
algorithms for Big Media Data ·
Compact
coding for Big Media Data ·
Benchmark
data ·
Knowledge
mining from Big Media Data ·
Algorithms
and applications with Big social media ·
Business
analytics for Big Media data ·
Other
applications of Big Media Data Submissions: All submissions to BMD 2015 must be written in English, and strictly follow the instruction of ICDM 2015 submission guidelines at http://icdm2015.stonybrook.edu/content/submission. Paper submissions will be reviewed by the selected experts based on their demonstrated knowledge of particular topics. The progress and results of the review process will be posted on this website, and authors will also be notified of the review results by email. Author(s) can now submit the papers at (https://wi-lab.com/cyberchair/2015/icdm15/scripts/submit.php?subarea=S03&undisplay_detail=1&wh=/cyberchair/2015/icdm15/scripts/ws_submit.php ) Important Dates Submission due: March 23, 2014 (extended to April 1, 2014) Author notification: April 9, 2014 (extended to April 11, 2014) Camera ready due: April 16, 2014 Workshop date: to be confirmed Organizers Jingdong
Wang, Microsoft Research Asia, China (welleast@outlook.com),
Address: No. 5 Danling Street, Haidian District, Beijing 100080, P.R.China Guo-Jun
Qi, University of Central Florida, USA (guojunq@gmail.com),
Address: University of Central Florida, EECS Department, 4328, corpius HEC
318, Orlando, FL 32816, USA Nicu
Sebe, University of Trento, Italy (sebe@disi.unitn.it)
Address: Dept. of Information Engineering and Computer Science, University of
Trento, via Sommarive 14, 38100 Povo - Trento, Italy Charu
Aggarwal, IBM T. J. Watson Research Center, USA (charu@us@ibm@com) Address:
1101 Kitchawan Road, Yorktown, NY 10598, USA |