July 2011 Newsletter
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This month's topics include:
Several special issues with Visual Communication and Image Representation (JVCI), Computer Vision and Image Understanding, and IEEE Multimedia
Special Issue on Sparse Representations for Image and Video Analysis Journal of Visual Communication and Image Representation (JVCI)
Sparse representation has gained popularity in the last few years as a technique to reconstruct a signal with few training examples. This reconstruction can be defined as adaptively finding a dictionary which best represents the signal on sample bases. Sparse representation establishes a more rigorous mathematical framework for studying high-dimensional data and ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms. The sparse representation has just been applied to visual analysis for few years, while has shown its advantages in processing the visual information. Thus it will have a great potential in this field. Sparse representation has wide applications in image/video processing, analysis, and understanding, such as denoising, deblurring, inpainting, compression, super-resolution, detection, classification, recognition, and retrieval. Many approaches based on sparse representation were proposed for these applications in the past years, and showed the promising results. This special issue aims to bring together the range of research efforts in sparse representation for image/video processing, analysis, and understanding. The goals of this special issue are threefold: (1) to introduce the advances of the theories on sparse representation; (2) to survey the progress of the applications of sparse representation in visual analysis; and (3) to discuss new sparse representation based technologies that will be potentially impactful in the image/video applications (primary results are needed).
The scope of this special issue is to cover all aspects that relate to sparse representation for visual analysis. Topics of interest include, but are not limited to the following:
The fundamental theories on sparse representation
Dictionary learning for sparse representation and modeling
The novel learning methods based on sparse representation
The applications of sparse representation in image/video denoising, impainting, debluerring, compression, and super-resolution
Sparse representation for pattern recognition and classification
Sparse representation for image/video retrieval
Sparse reconstruction for medical imaging and radar imaging
Sparse component analysis and its application to blind source separation
Information for Authors
Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the 'Journal of Visual Communication and Image Representation' at http://ees.elsevier.com/jvci/. When submitting via this page, please select "SparseRepresentations" as the Article Type. Prospective authors should submit high quality, original manuscripts that have not appeared, nor are under consideration, in any other journals. All submissions will be peer reviewed following the JVCI reviewing procedures.
Manuscript Submission Deadline: October 1, 2011
Notification of Acceptance/Rejection: January1, 2012
Final Manuscript Due to JVCI: April 1, 2012
Expected Publication Date: Fall 2012
Nanjing University of Science and Technology, China
National University of Singapore, Singapore
Microsoft Research Asia, China
University of Texas at San Antonio, USA
Tianjin University, China
Université Pierre et Marie Curie (UPMC- Paris 6), France
IEEE MULTIMEDIA (Special Issue) Large-Scale Multimedia Data Collections
Submission Deadline: 1 October 2011
Publication Issue: July-September 2012
Pivotal to many tasks in relation to multimedia research and development is the availability of a sufficiently large data set and its corresponding ground truth. Currently, most available data sets for multimedia research are either too small, such as the Corel or Pascal data sets; too specific, such as the Text Retrieval Conference Video (Trecvid) data set; or without ground truth, such as the recent efforts by the Massachusetts Institute of Technology and Microsoft Research Asia that gathered millions of Web images for testing. While it's relatively easy to crawl and store a huge amount of data, the creation of ground truth necessary to systematically train, test, evaluate, and compare the performance of various algorithms and systems is a major problem. For this reason, more and more research groups are individually putting efforts into the creation of such corpus to carry out research on large-scale data sets. There is a need to unify these individual efforts into the creation of a unified Web-scale repository that would benefit the entire multimedia research community.
The purpose of this special issue is to present and report on the construction and analysis of large-scale multimedia data sets and resources, and to provide a strong reference for multimedia researchers interested in large-scale multimedia data sets. The issue will specifically address the construction of data sets; the creation of ground truths; the sharing and extending of such resources in terms results and analysis related to ground truth, features, algorithms, and tools.
The IEEE MultiMedia special issue on large-scale multimedia data collections solicits original papers that will be of interest for IEEE MultiMedia readers. The list of possible topics includes, but is not limited to:
- Construction, unification, and evolution of corpus: the state of use, the lessons learned, and their impact, scalability of results, and range of applications.
- Framework for sharing of data sets, ground truths, features, algorithms, and tools, as well as comparison and analysis of results.
- Large-scale corpus analysis techniques: knowledge mining from large-scale multimedia corpus, optimization techniques on large-scale multimedia data for efficiency, and techniques for large-scale, content-based multimedia retrieval.
- Performance evaluation methodologies and standards.
For more information, please contact the Guest Editors:
Benoit Huet, EURECOM
Alexander Hauptmann, Carnegie Mellon University
Tat-Seng Chua, National University of Singapore
Submit your paper at https://mc.manuscriptcentral.com/cs-ieee. When uploading your paper, please select the appropriate special issue title under the category "Manuscript Type." If you have any questions regarding the submission system, please contact Andy Morton at email@example.com. All submissions will undergo a blind peer review by at least two expert reviewers to ensure a high standard of quality. Referees will consider originality, significance, technical soundness, clarity of exposition, and relevance to the special issue topics. All submissions must contain original, previously unpublished research or engineering work. Papers must stay within the following limits: 6,500 words maximum, 12 total combined figures and tables with each figure counting as 200 words toward the total word count, and 18 references.
To submit a paper to the July-September 2012 special issue, please observe the following deadlines:
1 October 2011: Full paper must be submitted using our online manuscript submission service and prepared according to the instructions for authors (please see the Author Resources page at http://www.computer.org/multimedia/author.htm).
15 January 2012: Authors notified of acceptance, rejection, or needed revisions.
5 April 2012: Final versions due.
Online Printable Version:
CALL FOR PAPERS
Computer Vision and Image Understanding: Special Issue on Visual Concept Detection
Bart Thomee, Yahoo! Research, Spain
Mark J. Huiskes, Leiden University, Netherlands
Michael S. Lew, Leiden University, Netherlands
Submission of manuscript: 1 November 2011
First notification of acceptance: 1 March 2012
Revised manuscript submission: 1 May 2012
Final notification of acceptance: 15 June 2012
Publication of special issue: Fall 2012
One of the grand challenges in multimedia information retrieval is automatic visual concept detection. This special issue calls on researchers that aim to raise the bar with novel approaches and techniques. All contributions are welcomed that address the topic of visual concept detection using the MIRFLICKR image collection, which is a popular large-scale open test benchmark. This special issue provides an excellent venue to publish high-quality work on novel ideas and insights that will significantly advance the state of the art.
The special issue centers around the MIRFLICKR image collection for the visual concept detection challenge. This set consists of one million images from thousands of real world users that were published to the Flickr social photography website under a creative commons license. To facilitate training and testing a subset of the collection has been carefully annotated by hand. The dataset can be obtained from http://mirflickr.liacs.nl. It is at the discretion of the authors to use the collection in its entirety or only partially.
Besides the annotations already supplied with the dataset, the ImageCLEF organization has additionally defined 99 concepts and 40 topics that can be expressed as a logical combination of these concepts. Their custom MIRFLICKR collection is available to registered participants of the ImageCLEF Photo Annotation task. Please refer to http://www.imageclef.org/2011/Photo for more details on this dataset. Results based on the ImageCLEF annotations are within the scope of this special issue.
All submissions for this special issue are required to follow the same format as regular full-length Computer Vision and Image Understanding papers. Manuscripts must be submitted through the CVIU online submission system at http://ees.elsevier.com/cviu. Please ensure to select 'Special Issue: Visual Concept Detection' as the 'Article Type'. All manuscripts should contain at least 30% original material. When submitting a manuscript that is an expanded version of a conference or workshop paper, this prior paper must be included as 'Supplementary Material' during submission. All manuscripts will be peer-reviewed according to the CVIU reviewing procedures.
If you have any questions, please contact Bart Thomee at firstname.lastname@example.org.