[visionlist] CFP: Special Issue on Image/Video Understanding and Analysis

[Xiaojun Chang]

*Call for Papers: (Special Issue for Pattern Recognition Letters) *

*Title: Image/Video Understanding and Analysis*

The explosive increase of multimedia data (i.e., text, image, and video) on
the Internet has brought the great challenge of how to effectively index,
retrieve and organize these resources. Much research attention has been
paid to understand and analyze the content in multimedia data. One can only
think to the amount of image/video data downloaded every minute in social
media or to the number of surveillance cameras installed in our cities
nowadays. Both scenarios require the researchers to develop automatic or
semi-automatic approaches which are able to mine discriminate information
from a large quantity of raw data.

This special issue aims to collect recent state-of-the-art achievement on
image/video understanding and analysis, especially the work devoted to
several new challenges in the field. Topics of interest include but not
limited to,

– Image/Video Understanding
– Classification and recognition
– Video content analysis
– Video segmentation
– Search and Retrieval
– Dimensionality reduction and manifold learning

*Submission Instruction:*

Authors should prepare their manuscript according to the Guide for Authors
available from the online submission page of Pattern Recognition Letters at
http://ees.elsevier.com/prletters. Specifically, authors should find the
acronym of the special issue visible to be selected as article type “IVUA”.
The maximal length of the submissions should be less than 10 pages in the
template of Pattern Recognition Letters which can be found in
http://ees.elsevier.com/prletters. Moreover, all the submissions should be
original and technically sound.

*Important Dates: *

Paper Submission: Apr 1-30, 2018
First Notification: Jun 15, 2018
Revised Manuscript: Jul 31, 2018
Final Manuscript Due: Oct 31, 2018

*Guest Editors: *

1. Xiaojun Chang (corresponding guest editor), Language Techniques
Institute, Carnegie Mellon University, Pittsburgh, PA, Email:
cxj273 at gmail.com

2. Xiaodan Liang, Machine Learning Department, Carnegie Mellon University,
Pittsburgh, PA, Email: xdliang328 at gmail.com

3. Yan Yan Department of Electrical Engineering and Computer Science,
University of Michigan, Ann Arbor, Michigan, Email: tom.yan.555 at gmail.com

4. Liqiang Nie School of Computer Science and Technology, Shandong
University, Jinan, Shandong, Email: nieliqiang at gmail.com
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