Visual information learning and analytics on cross-media big data
EURASIP Journal on Image and Video Processing welcomes submissions to the thematic series on "Visual Information Learning and Analytics on Cross-Media Big Data"
We are living in the era of data deluge. Meanwhile, the world of big data exhibits a rich and complex set of cross-media contents, such as text, image, video, audio and graphics. Thus far, great research efforts have been separately dedicated to big data processing and cross-media mining, with well theoretical underpinnings and great practical success. However, studies jointly considering cross-media big data analytics are relatively sparse. This research gap needs our more attention, since it will benefit lots of real-world applications. Despite its significance and value, it is non-trivial to analyze cross-media big data due to their heterogeneity, large-scale volume, increasing size, unstructured, correlations, and noise. Visual multimedia learning, which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved terrific progress in both academy and industry. From then on, deep learning has been adopted in all kinds of computer vision applications and many breakthroughs have achieved in sub-areas, like DeepFace on LFW competition for face verification, GoogleNet for ImageNet Competition for object categorization. It can be expected that more and more computer vision applications will benefit from Visual multimedia learning.
This special issue focuses on learning methods to achieve high performance Visual Multimedia analysis and understanding under uncontrolled environments in large scale, which is also a very challenging problem. Moreover, it attracts much attention from both the academia and the industry. We hope this topic will aggregate top level works on the new advances in Visual Multimedia analysis and understanding from big surveillance data. The purpose of this SI is to provide a forum for researchers and practitioners to exchange ideas and progress in related areas.
Potential topics include but are not limited to:
- Cross-Media Big Data Representation
- Large-scale multimodal media data acquisition
- Novel dataset and benchmark for cross-media big data analytics
- Cross-Media Big Data Management
- Large-scale multimodal information fusion
- Domain adaptation for cross-media big data
- Cross-media big data organization, retrieval and indexing
- Learning methods to bridge the semantic gap among media types
- Cross-Media Big Data Understanding and Applications
- Visual learning for feature representation
- Visual learning for face analysis
Before submitting your manuscript, please ensure you have carefully read the submission guidelines for EURASIP Journal on Image and Video Processing. The complete manuscript should be submitted through the EURASIP Journal on Image and Video Processing submission system. To ensure that you submit to the correct thematic series please select the appropriate thematic series in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the thematic series on "Visual Information Learning and Analytics on Cross-Media Big Data". All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
Deadline for submissions: 1 November, 2018
Lead Guest Editor
Zheng Xu, The Third Research Institute of the Ministry of Public Security & Tsinghua University, China
Junchi Yan, IBM Research, USA
Richard Y. D. Xu, University of Technology Sydney, Australia
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EURASIP Best paper awards 2017
We are pleased to announce that the following paper published in EURASIP Journal on Image and Video Processing has been awarded a EURASIP best paper award!
CU splitting early termination based on weighted SVM
Xiaolin Shen and Lu Yu
The award ceremony will take place at EUSIPCO 2017 in Greece. For more information see the EURASIP newsletter.