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CVIU Special Issue 2013 : Call for Papers: Computer Vision and Image Understanding (CVIU) special issue on Large-Scale Multimedia Semantic Indexing


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Categories    computer science   machine learning

Call For Papers

Computer Vision and Image Understanding Call for Papers
Special Issue on “Large Scale Multimedia Semantic Indexing”


The ever-increasing huge volume of multimedia data in Web-sharing sites and personal archives has provided us both challenges and unique opportunities on massive multimedia management. Due to the well-known semantic gap between human-understandable high-level semantics and machine-generated low-level features, recent years have witnessed plenty of research effort on large-scale multimedia content understanding and indexing. This special issue aims to collect recent state-of-the-art achievement on multimedia semantic indexing, especially the work devoted to several new challenges in this field. For example, it is recently discovered that facilitated with the contextual information in social multimedia, concept detection in videos can be better accomplished. Other problems such as cross-domain concept detection and multi-modality semantic learning are also in the interest of this special issue. Moreover, due to the explosive increase of both the size of the multimedia database and feature dimension, it is highly desired that an algorithm for semantic indexing or multimedia retrieval can be applied in a large-scale setting. An ideal algorithm should be a good balance between effectiveness and computation efficiency. Another focus of this special issue will be on recent advances on scalable algorithms. Particularly, locality-sensitive hashing (LSH) method which originates from theoretic computer science has attracted extensive research interest in the past years. Its success has been demonstrated in various applications, such as near-duplicate image detection. However, it is still an open problem how the hashing algorithm can be most effective, given the various complications in multimedia data, including diverse multimedia semantics, specific intrinsic data structure (e.g. graph or low-dimensional manifold) and multi-modality features. The special issue target at collecting latest research breakthroughs from both theoretic study and the related applications. Novel semantic indexing algorithms that are capable of handling large-scale data are highly appreciated. Inspiring work that discusses promising future directions is also welcome. This special issue targets the researchers and practitioners from both the industry and academia. Topics of interest include but not limited to:

• Ontology design and semantic concept detection
o Lexicon of semantic concepts
o Concept detection and semantic attribute extraction
o Novel feature representation and semantic indexing for image, audio and video data
o Cross-domain concept learning
o Semantics-oriented image and video annotation
o Fusion methods for multi-modality features
o Novel machine learning techniques for semantic features
o Concept detection in social multimedia

• Large scale semantic indexing algorithm
o Locality-sensitive hashing for multimedia semantic representation
o Hashing for complicated data structures (e.g., graphs, manifolds, multiple-instance data)
o Hashing in kernel space
o Hashing for multi-modality representation
o Benchmarks and evaluations for multimedia hashing

• Related applications
o Large-scale image or video retrieval
o Multimedia event detection
o Image annotation / tagging / recognition
o Query-adaptive methods for multimedia retrieval and event detection
o Large scale cross-media retrieval

Important Dates:
• Paper submission due: Aug. 15, 2013
• First notification: Nov. 15, 2013
• Revision: Dec. 30, 2013
• Final decision: March. 15, 2014
• Publication date: Autumn 2014 (Tentative)

Guest Editors:
• Dr. Yadong Mu, Columbia University, USA (
• Dr. Yi Yang, Carnegie Mellon University, USA (
• Dr. Liangliang Cao, IBM T.J. Watson Research Lab, USA (
• Prof. Shuicheng Yan, National University of Singapore (
• Prof. Qi Tian, University of Texas at San Antonio, USA (

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