Welcome to the April’15 edition of the IEEE-TCMC (Technical Committee on Multimedia
Computing) monthly mailing.
This month’s topics include:
Journal SI CFPs:
IEEE Trans. on Multimedia SI (due Apr. 20th)
IEEE Trans. Multimedia Call for Papers in Emerging Multimedia Areas
IEEE Trans. on Multimedia: TCMC call for Special Issue and Survey/overview proposals
(Due Apr. 25th to TCMC chair: Dr. Shu-Ching Chen)
Journal ToC and Abstracts:
IEEE Multimedia April-June TOB
Abstract Announcement for International Journal of Multimedia Data Engineering and Management (IJMDEM) 6(1)
To join TCMC, or to update your information, especially your email address, visit
the following web site and fill in the online form.
https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage….html?product=CMYMC717
Computer Society and IEEE members will use their usual IEEE web account login to
access membership products and renew. Nonmembers can create an IEEE web account to
join any TC.
TCMC home: http://www.computer.org/web/tcmc
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Call For Papers
IEEE Transactions on Multimedia Special Issue on Deep Learning for Multimedia
Computing
Deadline: April 20th
Summary: Conventional multimedia computing is often built on top of handcrafted
features, which are often much restrictive in capturing complex multimedia content
such as images, audios, text and user-generated data with domain-specific knowledge.
Recent progress on deep learning opens an exciting new era, placing multimedia
computing on a more rigorous foundation with automatically learned representations
to model the multimodal data and the cross-media interactions. Existing studies have
revealed promising results that have greatly advanced the state-of-the-art
performance in a series of multimedia research areas, from the multimedia content
analysis, to modeling the interactions between multimodal data, to multimedia
content recommendation systems, to name a few here.
This special issue aims at providing a forum to present recent advancements in deep
learning research that directly concerns the multimedia community. Specifically,
deep learning has successfully designed algorithms that can build deep nonlinear
representations to mimic how the brain perceives and understands multimodal
information, ranging from low-level signals like images and audios, to high-level
semantic data like natural language. For multimedia research, it is especially
important to develop deep networks to capture the dependencies between different
genres of data, building joint deep representation for diverse modalities.
Scope: The topics of interest include but are not limited to 1. Novel deep network
architectures for multimodal data 2. Efficient training and inference methods for
multimedia deep networks 3. Emerging applications of deep learning in multimedia
search, retrieval and management 4. Deep learning for multimedia content analysis
and recommendation 5. Deep learning for cross-media analysis, knowledge transfer and
information sharing 6. Distributed computing, GPUs and new hardware for deep
learning in multimedia research 7. Other deep learning topics for multimedia
computing, involving at least two modalities
Submission guideline: Prospective authors should submit original manuscripts that
have not appeared, nor are under consideration, in any other journals. Prospective
authors are required to strictly follow the Author’s Guide for manuscript submission
to the IEEE Transactions on Multimedia (TMM) at
http://www.signalprocessingsociety.org/tmm/tmm-author-info/, and manuscripts should
be submitted electronically through the online IEEE manuscript submission portal at
http://mc.manuscriptcentral.com/tmm-ieee.
Important Dates Paper submission due: April 20, 2015 (extended) First-round review
completed: June 1, 2015 Revision Due: July 1, 2015 Second-round review completed:
August 1, 2015 Final manuscript due: September 1, 2015 Publication date:
November/December 2015
Guest Editors (in alphabetic order of last name) Dr. Benoit Huet, Eurecom, France
Dr. Hugo Larochelle, University de Sherbrooke, Canada Dr. Jiebo Luo, University of
Rochester, USA Dr. Guo-Jun Qi, University of Central Florida, USA Dr. Kai Yu, Baidu
Inc., China Senior adviser: Prof. Thomas Huang, University of Illinois at
Urbana-Champaign, USA
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IEEE Trans. Multimedia Call for Papers in Emerging Multimedia Areas
In January 2015, IEEE Trans. Multimedia (T-MM) has reached one of its major
milestones: T-MM now publishes 12 monthly issues a year. This significant growth
from eight (8) issues per year to 12 issues per year shall demand more paper
submission from T-MM community! The number of published issues represents a
50% growth from 2014 to 2015! This significant growth of T-MM publication is also
the direct results of extremely fast evolution of the multimedia fields. We have
witnessed increasing expansion of multimedia related research, making the EDICS
of T-MM unable to cover a good portion of the submitted papers in several
emerging areas.
To meet the demand in the healthy growth of multimedia fields and to accommodate
the submissions that may appear to be outside the current T-MM EDICS, T-MM is
setting up a “Special Track” specifically for the submission of papers in constantly
evolving multimedia emerging areas.
The editorial board of T-MM has identified the following topics to be among the
emerging areas:
1. SHEU – Multimedia for social activities in health, environment, and urban living
2. MWIM – Multimedia for personal applications (mobile, wearables, interactive)
3. ARVL – Multimedia for augmented experience in real and virtual life
4. IMSP – Multimedia for immersive search space and personalized recommendations
5. ULDN – Ultra low delay next generation multimedia communication and networking
6. CMPG – Cloud media platforms and innovative applications and cloud gaming
7. NPCD – Multimedia networking and processing in the cloud and data centers
8. ICNF – Multimedia in Information Centric Networks and Future Internet
9. HDRU – HDR and Ultra HD multimedia processing and communications
10. 5GMN – Multimedia support for 5G and beyond 5G mobile networks
11. MIOT – Multimedia Internet of Things on digital home and lifestyle, on-device processing
12. BDCS – Big data analytics on multimedia data and crowd sourcing for multimedia applications
13. STCM – Multimedia storytelling and cross-modal translations between multimedia contents
14. UESV – Ultra-efficient surveillance video and coding of multimedia features (instead of data)
15. SLAM – Speech, language and audio in video analysis; Music in multimedia
16. TPSM – Trust in social multimedia and privacy-protecting multimedia analysis
17. CPSM – Cyber-Physical-Social spaces for multimedia applications and smart city
18. UCSM – User-centric social multimedia computing and location based multimedia service
19. MSAM – Multimedia sentiment analysis and synthesis; affective multimedia processing
20. MMSR – Multimodal signal representation and visualization
Authors who are working in these emerging areas are cordially invited to submit their papers to this
“Special Track.” These papers will receive fast track review priority by an AE who are familiar with the
topics. When submitting your paper in these areas, please select “Emerging Multimedia Areas” Category.
When selecting EDICS, please select from 20 topics under “10 EMERGING TOPICS IN MULTIMEDIA”
Our current average days from submission to first decision is around 65. We expect papers submitted to
this special track will reach first decision well under 60 days. This is comparable to the review time for
most multimedia conferences. I encourage everyone to consider T-MM as your first choice for submission.
Chang Wen Chen
Editor-in-Chief, IEEE Trans. on Multimedia
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Dear TCMC members:
Please submit your T-MM Special Issue Proposal and survey/overview proposal to
Dr. Shu-Ching Chen (chens@cs.fiu.edu, TCMC chair) by April 25.
TCMC will form a committee to evaluate these proposals and select the best
proposals to T-MM.
Call for Proposals for IEEE Trans. Multimedia Special Issues
Call for proposals for IEEE Trans. Multimedia (T-MM) special issues is now open to all members of the
community. We would like to once again rely on four sponsoring Technical Committee (TC)’s help to
identify the best proposals from among its members. The proposals need to be submitted to each TC
first and the topics should be consistent with emerging areas specific to each TC. Proposals that
addresses topics across more than one TC’s are also encouraged and joint support from multiple TC’s
may be worked out for such proposal.
Each TC should select among its own proposals and submit to T-MM Editor-in-Chief (EiC) no more than
two (2) proposals for discussion and ranking by the T-MM Editorial Board. The deadline for TC
submission to EiC will be May 15, 2015. The final ranking of proposals from all TC’s will be completed by
the Editorial Board around June 1, 2015. These ranked special issue proposals will then be forwarded to
the Steering Committee for approval before ICME2015 for promotion at ICME2015. Once approved, the
teams shall work with EiC for the schedule of the special issues. It is expected that at least two special
issues will be approved for 2016.
It is advised that the team of Guest Editors should not exceed four (4) people. Balance between
academic and industry as well as geographical representations should be considered when forming the
guest editor team. The proposal should have a draft call-for-proposal and should also include the wellthought
rationale for the proposed topics. Biographies of the team members should be included to
show their qualifications and relevant experiences. The proposal should also identify the potential
source of the submissions and an estimate of the number of submissions.
Call for Proposals for IEEE Trans. Multimedia Survey/Overview Papers
Call for proposals for IEEE Trans. Multimedia (T-MM) survey/overview papers is now open to all
members of the community. We would also like to call for TC’s help to identify the best proposals from
among its members. The proposals need to be submitted to your own TC first and the topics should be
consistent with emerging areas specific to each TC. Proposals that addresses topics across more than
one TC’s are also encouraged. In general, the number of the authors should be limited to four (4) and
each team should include at least one senior leader (preferably IEEE Fellow) in the research areas
consistent with the proposed topics. A balanced team of co-authors is preferred.
Each TC should select among its own proposals and submit to T-MM Editor-in-Chief (EiC) no more than
two (2) proposals for discussion and ranking by the T-MM Editorial Board. The deadline for TC
submission to EiC will be May 15, 2015. The final ranking of proposals from all TC’s will be completed by
the Editorial Board around June 1, 2015. These ranked survey/overview paper proposals will be
forwarded to the Steering Committee for approval and the results will be announced before ICME2015.
Once approved, the authors of the approved survey/overview papers shall work with EiC for the
schedule of the submission and review process. It is expected that up to four (4) survey/overview papers
may be approved for 2016.
It is advised that the survey/overview paper proposals should include an executive summary and an
outline of the paper. When forming the team of co-authors, balance between academic and industry as
well as geographical representation should be considered. The proposal should also include wellthought
rationale for the proposed topics and biographies of the co-authors to show their qualifications
and relevant experiences.
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IEEE MultiMedia, April–June 2015 vol. 22, no. 2
TABLE OF CONTENTS
Multimedia Goes Beyond Content
24 A Novel Markov Logic Rule Induction Strategy for Characterizing Sports Video Footage
David Windridge, Josef Kittler, Teofilo de Campos, Fei Yan, William Christmas, and Aftab Khan
36 Bidirectional Mesh-Based Frame Rate Up-Conversion
Kyung-Yeon Min, Jong-Hyun Ma, Dong-Gyu Sim, and Ivan V. Bajić
46 Saliency-Guided Deep Framework for Image Quality Assessment
Weilong Hou and Xinbo Gao
56 A Survey of Current YouTube Video Characteristics
Xianhui Che, Barry Ip, and Ling Lin
64 Media Contract Formalization Using a Standardized Contract Expression LanguageEva Rodríguez, Jaime Delgado, Laurent Boch, and Víctor Rodríguez-Doncel
Departments
2 EIC’s Message
Yong Rui
Multimedia Goes Beyond Content
4 Research Projects
Kiyoharu Aizawa and Makoto Ogawa
FeedLog: Multimedia Tool for Healthcare Applications
10 Multimedia Impact
Daniel Gayo-Avello
Social Media, Democracy, and Democratization
18 Artful Media
Aisling Kelliher
Machines Learning Culture
23 Book Reviews
Susanne Boll
Multimedia Computing Offers a Multisensory Perspective
76 Visions and Views
Hatice Gunes and Hayley Hung
Emotional and Social Signals: A Neglected Frontier in Multimedia Computing?
86 Scientific Conferences
Xiangjian He, Suhuai Luo, Dacheng Tao, Changsheng Xu, and Jie Yang
The 21st International Conference on MultiMedia Modeling
Advertising index, p. 55
IEEE CS Information, p. 74
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Abstract Announcement for International Journal of Multimedia Data Engineering and
Management
(IJMDEM) 6(1)
The contents of the latest issue of: International Journal of Multimedia Data
Engineering and Management (IJMDEM) Volume 6, Issue 1, January – March 2015
Published: Quarterly in Print and Electronically ISSN: 1947-8534; EISSN: 1947-8542;
Published by IGI Global Publishing, Hershey, USA www.igi-global.com/ijmdem
Editor(s)-in-Chief: Shu-Ching Chen (Florida International University, USA)
Note: There are no submission or acceptance fees for manuscriptssubmitted to the
International Journal of Multimedia Data Engineering and Management (IJMDEM). All
manuscripts are accepted based on a double-blind peer review editorial process.
ARTICLE 1
Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled
Environments
Dianting Liu (Department of Electrical and Computer Engineering,University of Miami,
Coral Gables, FL, USA), Yilin Yan (Department of Electrical and Computer
Engineering, University of Miami, Coral Gables, FL, USA), Mei-Ling Shyu (Department
of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA),
Guiru Zhao (China Earthquake Networks Center, Beijing, China), Min Chen (Computing
and Software Systems, University of Washington Bothell, Bothell, WA, USA)
Understanding semantic meaning of human actions captured in unconstrained
environments has broad applications in fields ranging from patient monitoring,
human-computer interaction, to surveillance systems. However, while great progresses
have been achieved on automatic human action detection and recognition in videos
that are captured in controlled/constrained environments,most existing approaches
perform unsatisfactorily on videos withuncontrolled/unconstrained conditions (e.g.,
significant camera motion, background clutter, scaling, and light conditions). To
address this issue, the authors propose a robust human action detection and
recognition framework that works effectively on videos taken in controlled or
uncontrolled environments. Specifically, the authors integrate the optical flow
field and Harris3D corner detector to generate a new spatial-temporal information
representation for each video sequence, from which the general Gaussian mixture
model (GMM) is learned. All the mean vectors of the Gaussian components in the
generated GMM model are concatenated to create the GMM supervector for video action
recognition. They build a boosting classifier based on a set of sparse
representation classifiers and hamming distance classifiers to improve the accuracy
of action recognition. The experimental results on two broadly used public data
sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the
other state-of-the-art approaches on both actiondetection and recognition.
To obtain a copy of the entire article, click on the link
below.www.igi-global.com/article/spatio-temporal-analysis-for-human-action-detection-and-recognition-in-uncontrolled-environments/124242
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=124242
ARTICLE 2
Comparison of Image Decompositions Through Inverse Difference and Laplacian Pyramids
Roumen Kountchev (Radiocommunications Department, Technical University of Sofia,
Sofia, Bulgaria), Stuart Rubin (Space and Naval Warfare Systems Center, San Diego,
CA, USA), Mariofanna Milanova (Department of Computer Science, University of
Arkansasat Little Rock, Little Rock, AR, USA), Roumiana Kountcheva (T & K
Engineering, Sofia, Bulgaria)
In this work, the Inverse Difference Pyramid (IDP) and its modification – the
Reduced IDP (RIDP), are compared and evaluated with the famous Laplacian Pyramid
(LP) for multi-leveldecomposition of digital images. The comparison comprises: the
structures of LP and IDP, the image representation through LP and IDP in the pixel
space and in the spectrum space of the Fourier transform, the efficiency of both
transforms for the aims of the progressive image transfer, the LP and the IDP
computation graphs and the evaluation of the computational complexity of the
algorithms for the 3-level reduced LP and the 3-level reduced IDP. The influence of
the quantization noise on both decompositions is also analyzed. On the basis of the
comparison are outlined the basic advantages of the RIDP for pipeline image
processing. The results obtained could be used for the design of coders for image
compression, aimed at real-time applications, etc.
To obtain a copy of the entire article, click on the link
below.www.igi-global.com/article/comparison-of-image-decompositions-through-inverse-difference-and-laplacian-pyramids/124243
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=124243
ARTICLE 3
Image Segmentation Utilizing Color-Space Feature
Mohammad A. Al-Jarrah (Computer Engineering Department, Yarmouk University, Irbid,
Jordan)
In this paper, the authors introduced a stochastic model for color images. Utilizing
this model, they proposed a new method for color image segmentation. The proposed
method consists of three stages; the first stage considers the red, green, and
bluecolor component of the image as a gray image. One of the known gray image
Thresholding algorithm is applied on the three color components. The second stage
segments the image based on the results of first stage. This stage produces eight
color segments. The third stage identifies the segments through color-space
correlation. Color-space correlation algorithm assumes that a set of pixels are
considered to belong to one region if and only if they belong to the same color
cluster and all connected using neighborhood filters. The last stage may produce
very small segments. These small segments are merged with their closed neighbors
based on color features. Finally, Conducted experiments achieved perceptually
accepted segments and compare favorably to other segmentation methods.
To obtain a copy of the entire article, click on the link
below.www.igi-global.com/article/image-segmentation-utilizing-color-space-feature/124244
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=124244
ARTICLE 4
A Review on Semantic Text and Multimedia Retrieval and Recent Trends
Oguzhan Menemencioglu (Department of Computer Engineering, Karabük University,
Karabük, Turkey), Ilhami Muharrem Orak (Department of Computer Engineering, Karabük
University, Karabük, Turkey)
Semantic web works on producing machine readable data and aims to deal with large
amount of data. The most important tool to access the data which exist in web is the
search engine. Traditional search engines are insufficient in the face of the amount
of data that consists in the existing web pages. Semanticsearch engines are
extensions to traditional engines and overcome the difficulties faced by them. This
paper summarizes semantic web, concept of traditional and semantic search enginesand
infrastructure. Also semantic search approaches are detailed. A summary of the
literature is provided by touching onthe trends. In this respect, type of
applications and the areas worked for are considered. Based on the data for two
different years, trend on these points are analyzed and impacts of changesare
discussed. It shows that evaluation on the semantic web continues and new
applications and areas are also emerging. Multimedia retrieval is a newly scope of
semantic. Hence, multimedia retrieval approaches are discussed. Text and multimedia
retrieval is analyzed within semantic search.
To obtain a copy of the entire article, click on the link
below.www.igi-global.com/article/a-review-on-semantic-text-and-multimedia-retrieval-and-recent-trends/124245
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=124245
For full copies of the above articles, check for this issue of the International
Journal of Multimedia Data Engineering and Management (IJMDEM) in your institution’s
library. This journal is also included in the IGI Global aggregated
“InfoSci-Journals”database: www.igi-global.com/isj.
Indices of IJMDEM:
ACM Digital Library
Bacon’s Media Directory
Cabell’s Directories
DBLP
Google Scholar
INSPEC
Journal TOCs Library & Information Science Abstracts (LISA)
MediaFinder
The Standard Periodical Directory
Ulrich’s Periodicals Directory
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