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MTAP-1083T 2017 : MTAP Special Issue on Data Preprocessing for Big Multimedia Data

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Link: http://static.springer.com/sgw/documents/1603333/application/pdf/DPBMD_MTAP_CFP.pdf
 
When N/A
Where N/A
Submission Deadline Aug 1, 2017
Notification Due Sep 15, 2017
Final Version Due Dec 1, 2017
Categories    computer science   multimedia   big data   data processing
 

Call For Papers

Call for Papers
Special Issue on Data Preprocessing for Big Multimedia Data
Multimedia Tools and Applications, Springer Journal
http://www.springer.com/journal/11042
Summary and Scope:
Internet revolution has enabled us to acquire and gather massive amount of multimedia
data relatively easily. However, a lot of issues appear in obtaining and processing such big multimedia data, such as data heterogeneity, data incompleteness (data missing), high-dimensionality of data, etc. Moreover, many multimedia data sets simultaneously contain one or more of these issues. This makes the learning of big multimedia data difficult as most of the current techniques can only deal with homogeneous, complete, and moderate-sized-dimensional data. Hence, there is a huge gap between the current machine learning techniques and the requirements of our real life. In this case, data preprocessing (such as data representation learning, dimensionality reduction, missing value imputation, etc) should be very interesting and challenging to relief such a gap.

The goal of this proposal is to attract articles that cover existing aforementioned issues in data preprocessing of multimedia data. We would also like to accept successful applications of the new methods, including but not limited to data processing, analysis, and knowledge discovery of big multimedia data. Below is an incomplete list of potential topics to be covered in the special issue:
o Feature extraction by deep learning or sparse codes for multimedia data
o Data representation of multimedia data
o Dimensionality reduction techniques (subspace learning, feature selection,
sparse screening, feature screening, feature merging, etc) for multimedia data
o Information retrieval for multimedia data
o Kernel-based learning for multi-source multimedia data
o Incremental learning or online learning for multimedia data.
o Data fusion for multi-source multimedia data
o Missing data imputation for multi-source multimedia data
o Data management and mining in multimedia data
o Web search and meta-search for multimedia data
o Web information retrieval for multimedia data
o Multimedia data quality assessment

Submission Guideline
Authors should prepare their manuscripts according to the Instructions for Authors
available from the online submission page of the Multimedia Tools and Applications at
https://www.editorialmanager.com/mtap/default.aspx. Submitted papers should
present original, unpublished work, relevant to one of the topics of the Special Issue.
All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.

Important Dates:
Paper submission due: August 1, 2017
First notification: September 15, 2017
First revision due: Oct 30, 2017
Final decision: Dec 1, 2017

In addition, if you are able to review paper in this special issue, please contact the guest editor at henrythung at gmail dot com.

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