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Special issue on visual information retrieval

Special issue on visual information retrieval Int J Multimed Info Retr (2016) 5:1–2 DOI 10.1007/s13735-016-0094-7 EDITORIAL Michael S. Lew Published online: 27 January 2016 © Springer-Verlag London 2016 engine for internet videos” by Lu Jiang, Shoou-I Yu, Deyu Meng, Teruko Mitamura and Alexander G. Hauptmann, the authors present a semantic video search system which does not use any user meta-data and also had the best performance from the well known NIST TRECVID 2014 competition. They also provide several interesting recommendations for future video search systems. Another leading approach to video search is to make the search adaptive to the user, that is, to use any multi-modal data linked to the user. In the paper, “User-adaptive image retrieval via fusing pointwise and pairwise labels” by Lin Chen, Peng Zhang and Baoxin Li, the authors propose a hybrid ranking framework which results in significant performance This special issue provides an overview of important work improvements. They also found that their online learning and the leading research directions in the area of visual infor- approach has superior performance as compared to batch mation retrieval. With the flood of images and video from learning. diverse sources (e.g., smartphones, NetFlix, FLICKR, Ama- In the classic visual learning method there needs http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Special issue on visual information retrieval

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer-Verlag London
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl. Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-016-0094-7
Publisher site
See Article on Publisher Site

Abstract

Int J Multimed Info Retr (2016) 5:1–2 DOI 10.1007/s13735-016-0094-7 EDITORIAL Michael S. Lew Published online: 27 January 2016 © Springer-Verlag London 2016 engine for internet videos” by Lu Jiang, Shoou-I Yu, Deyu Meng, Teruko Mitamura and Alexander G. Hauptmann, the authors present a semantic video search system which does not use any user meta-data and also had the best performance from the well known NIST TRECVID 2014 competition. They also provide several interesting recommendations for future video search systems. Another leading approach to video search is to make the search adaptive to the user, that is, to use any multi-modal data linked to the user. In the paper, “User-adaptive image retrieval via fusing pointwise and pairwise labels” by Lin Chen, Peng Zhang and Baoxin Li, the authors propose a hybrid ranking framework which results in significant performance This special issue provides an overview of important work improvements. They also found that their online learning and the leading research directions in the area of visual infor- approach has superior performance as compared to batch mation retrieval. With the flood of images and video from learning. diverse sources (e.g., smartphones, NetFlix, FLICKR, Ama- In the classic visual learning method there needs

Journal

International Journal of Multimedia Information RetrievalSpringer Journals

Published: Jan 27, 2016

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