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SI: CAfBMD 2016 : Springer Multimedia Tools and Applications Special Issue on Content Analysis for Big Multimedia Data

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Link: http://static.springer.com/sgw/documents/1589141/application/pdf/CFP_MTA_Content_Analysis_for_Big_Multimedia_Data.pdf
 
When N/A
Where N/A
Submission Deadline Dec 1, 2016
Notification Due Jan 15, 2017
Final Version Due May 1, 2017
Categories    multimedia   computer vision   image processing   machine learning
 

Call For Papers

Springer Multimedia Tools and Applications
Special Issue on Content Analysis for Big Multimedia Data


During the past decades, multimedia data grows exponentially. A large amount of images and videos are shared through public websites such as Flickr, Facebook, and YouTube. This big data brings us the opportunity to understand the world better, meanwhile, giving rise of the challenges of interpreting the data. Sufficiently analyzing this large amount of multimedia data becomes essential to understand the content exhibited in the images and the videos, which could significantly enhance applicable usages of this large amount of multimedia data. Nevertheless, how to efficiently process and explore the multimedia data is still a critical problem in understanding images and videos. Therefore, extracting accurate information from the multimedia data and building an effective analyzing framework are critical to better make use of the big multimedia data.

This special issue aims at collecting recent advances in large-scale multimedia data analysis, as well as providing an opportunity for researchers around the world to discuss their research outcomes. We are soliciting state-of-the-art research papers as well as literature reviews. Both theoretical and experimental outcomes are welcome to be submitted. Specific topics we are interested including, but not limited to are:

• Machine learning methods for image and video understanding
• Multimedia search and retrieval
• Large-scale 3D reconstruction and registration
• Multimedia datasets collection methods
• Feature extraction methods for image and video representation
• Application of transfer learning for cross-modality multimedia understanding
• Deep Learning for multimedia data analysis
• Real-world applications based on multimedia analysis, e.g., object detection and recognition, image segmentation, image and video categorization, scene understanding, attribute learning
• Survey papers with regards of topics of content analysis for big multimedia data.

Important dates:

Paper submission: December 1, 2016 (Original deadline Oct. 1, 2016)
First Notification: January 15 2017
Revised Manuscript: March 1, 2017
Notification of Acceptance: April 1, 2017
Final Manuscript Due: May 1 2017


Guest Editors:

Dr. Guoyu Lu, Advanced Technology Group, ESPN, USA, Email: luguoyu62@gmail.com

Dr. Yan Yan, Department of Information Engineering and Computer Science, University of Trento, Italy, Email: yan@disi.unitn.it

Prof. Chandra Kambhamettu, Department of Computer and Information Science, University of Delaware, USA, Email: chandrak@udel.edu

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