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Multimedia Tools and Application
Special Issue on
The Role of Hand-crafted and Learned Representations for Multimedia
Call for Papers
Multimedia is considered to be the most valuable and effective means of communication.
Recent advancements in multimedia technologies have caused enormous increase in the
volume of multimedia production and consumption. This exponential growth in
multimedia databases has introduced numerous challenges to the research community
including, their management, security, transmission, retrieval, and mining. Intelligent
algorithms for multimedia processing and manipulations often rely on some
representational form of the multimedia data (images and videos). These intermediate
representational forms play a fundamental role in the efficient utilization of the huge
volumes of multimedia databases available to us today. Conventional representational
methods mostly relied on hand-crafted features which have been widely explored and
used in many multimedia applications over the past two decades. More recently, feature
learning has drawn significant interest in the multimedia community. Both hand-crafted
and learned representations have their advantages in multimedia representations, which
need to be explored further for improving the current multimedia applications.
Hand-crafted feature representations are flexible, computationally efficient, and do not
need huge datasets for training. On the other hand, feature learning (deep learning)
methods are able to learn representations directly from raw data for use in specific tasks.
In addition, both these schemes can be used together to achieve improvements in such
representations. It is always desired to obtain efficient and effective representations using
conventional hand-crafted method or deep learning in order to build high performance
multimedia applications. With this special issue, it is desired to bring together researchers
from computer vision, machine learning, and pattern recognition communities for
advancements in multimedia representations. Relevant topics include, but are not limited
Efficient hand-crafted feature engineering for image and video analysis
Multimodal features extraction for video summarization
Unsupervised feature learning using convolutional neural networks
Sparse coding for surveillance video analysis and retrieval
Exploring transfer learning strategies for utilizing deep architectures in multimedia
Exploiting hand-crafted and learned features for object recognition, localization, and
Features extraction for multimedia indexing and retrieval
Intelligent algorithms for multimedia security
Big multimedia data mining
Sparse feature learning for super resolution
Multimedia systems design using hybrid feature sets
Efficient and secure transmission of multimedia data
Authors should prepare their manuscript according to the Guide for Authors available
from the online submission page of the Multimedia Tools and Applications at All the papers will be peerreviewed following the Multimedia Tools and Applications reviewing procedures.
Important Dates
Paper Submission: April 30, 2016
Revision/Acceptance Notification: June 30, 2016
Revised Manuscript Due: August 15, 2016
Final Decision Notification: September 15, 2016
Lead Guest Editor
Sang-Soo Yeo, Mokwon University, Daejeon, Republic of Korea
([email protected])
Guest Editors
Daqiang Zhang, Tongji University, China ([email protected])
Irfan Mehmood, COMSATS Institute of Information Technology, Attock, Pakistan
([email protected])