Journal Information
Multimedia Tools and Applications
http://www.springer.com/computer/information+systems+and+applications/journal/11042
Impact Factor:
1.53
Publisher:
Springer
ISSN:
1380-7501
Viewed:
8606
Tracked:
31

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Call For Papers
Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools, and case studies of multimedia applications. Experimental and survey articles are appropriate for the journal. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed.

Specific areas of interest include (but are not limited to):

Multimedia Tools:
Multimedia application enabling software
System software support for multimedia
Hypermedia
Performance measurement tools for multimedia
Multimedia authoring tools
System hardware support for multimedia
Multimedia databases and retrieval
Web tools and applications
Multimedia Applications:
Prototype multimedia systems and platforms
Multimedia on information superhighways

Home
Video on-demand
Interactive TV
Home shopping
Remote home care
Electronic album
Personalized electronic journals
Last updated by Dou Sun in 2017-09-15
Special Issues
Special Issue on Spatial-Temporal Feature Learning for Unconstrained Video Analysis
Submission Date: 2017-12-15

With the development of mobile Internet and personal devices, we are witnessing an explosive growth of video data on the Web. This has encouraged the research of video analysis. Compared to the trimmed videos in the open benchmark datasets, most of the real-world videos are unconstrained. Firstly, as captured under different conditions, unconstrained videos usually have large intra-class differences. Secondly, as captured by different devices and people, unconstrained videos may own more variants in quality. The success of hand-crafted descriptors lies in the simultaneously incorporating spatial description of each frame and temporal consistency of successive frames. Recently, researchers have tried to learn video representations from deep ConvNets, where the promising progresses were obtained owing to the breakthrough in the appropriately pooling or encoding of temporal information of video sequences in the deep neural networks. As the visual content and temporal consistency of unconstrained videos are more complex, there are still challenges in video analysis and practical applications. This special issue serves as a forum for researchers all over the world to discuss their works and recent advances in video feature learning and its applications in real world applications. Both state-of-the-art works, as well as benchmark datasets and literature reviews, are welcome for submission. This special issue seeks to present and highlight the latest developments on practical methods of unconstrained video analysis. Papers addressing interesting real-world applications are especially encouraged. Topics of interest include, but are not limited to, - Feature learning by multi-cue fusion for unconstrained video analysis - Pooling the spatial-temporal layers in deep ConvNets - End-to-end integration of RNN and CNN for video feature learning - Effective feature learning for video captioning - Ad hoc feature learning for video event detection - Adapt un-labeled videos for robust feature learning - Transfer feature learning for video analysis - Spatial-temporal hashing and indexing for large-scale video retrieval - Unconstrained video benchmark for the evaluation of future learning - Real-world applications of unconstrained video analysis with future learning, e.g., event detection, action recognition, retrieval, summarization, synthesis, and video-to-language captioning.
Last updated by Dou Sun in 2017-06-14
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