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MTAP SI on Image Analysis and Processing 2015 : MTAP (Springer): Special Issue on “Image Analysis and Processing Leveraging External Data”


When N/A
Where N/A
Submission Deadline Mar 15, 2015
Notification Due May 15, 2015
Final Version Due Sep 15, 2015
Categories    image processing   image analysis   multimedia

Call For Papers

Multimedia Tools and Applications (MTAP)-Springer
Special Issue on “Image Analysis and Processing Leveraging External Data”

Aim and scope

There is a broad range of multimedia analysis and processing applications that rely on accurate understanding of images. However, accurately understanding what the underlying visual content represents still remains a very challenging problem. Although many advances in feature extraction and machine learning have been made recently leading to improved image understanding, purely content-based techniques are often unsatisfactory for practical real-world problems (involving large number of classes, subtle differences, and unconstrained domains).

Actually, humans do not solve visual problems based just on the captured pixel data. Many non-visual clues and diverse types of information are exploited, including learned prior knowledge, personal past experience and contextual information. This additional enriched information is critical to accurately understand the scene and process it accordingly.

In the same sense, automatic image analysis and processing systems must welcome and exploit additional help when it is available. Many application scenarios often meet this situation. For example, mobile phones and other devices can capture a variety of rich contextual information of the scen, and at the same time, task-specific knowledge and data sources are often available and accessible online. Examples of helpful sources may include prior knowledge, social context, web data, related images from personal collections and online sources.

In this special issue we want to explore new techniques using other sources of knowledge and contextual information, to solve more effectively practical image analysis and processing problems. We are particularly interested in problems that current techniques cannot solve relying on only the visual content, but that can be solved much more reliably by exploiting other external sources. The aim of this special issue is to cover the state-of-the-art research in this topic and provide new directions for future research.

Relevant topics of interest include (but not limited to):
• Context-assisted large-scale image understanding.
• Enhanced image processing.
• Mobile visual recognition using multi-sensor data.
• Context-assisted mobile image search and retrieval.
• Ontology-driven analysis and processing.
• Social media and online data for image analysis and processing.
• Geocontext for image annotation and processing.
• Context-aware image processing and recommendation.
• Novel sources of information applied to image analysis.
• Novel applications exploiting contextual data.

Submission Guidelines

Prospective authors should submit high quality, original manuscripts that have not been published, nor being currently under consideration for publication by other journals or conferences. Extended conference papers may be submitted more than 50% has been completely rewritten. A separate cover letter should be submitted that includes the paper title, the list of all authors and their affiliations, and information of the contact author. All submissions will be rigorously peer reviewed following the MTAP reviewing procedures. Prospective authors are invited to submit their papers directly via the online submission system at Authors have to select Choose Article Type “Image Analysis and Processing Leveraging External Data”. Submissions should follow the author guidelines (

Important Dates (tentative):

Manuscript Submission: March 15, 2015
First Round Decision: May 15, 2015
Revised Manuscript: July 1, 2015
Notification of Acceptance: Aug. 15, 2015
Final Manuscript: Sep. 15, 2015

Guest Editors

Luis Herranz, Institute of Computing Technolgy, Chinese Academy of Sciences, (
Jian Cheng, Institute of Automation, Chinese Academy of Sciences (
Yue Gao, National University of Singapore (
Shuqiang Jiang, Institute of Computing Technology, Chinese Academy of Sciences (

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