Multimedia Tools and Application

Special Issue on The Role of Hand-crafted and Learned Representations for Multimedia Applications

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 to:

 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 applications  Exploiting hand-crafted and learned features for object recognition, localization, and tracking  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 https://www.editorialmanager.com/mtap/default.aspx. All the papers will be peer- reviewed 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])