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RETIN: a smart interactive digital media retrieval system

Published: 09 July 2007 Publication History

Abstract

This demonstration presents a digital media retrieval system for searching large categories in different media databases. The core of our system is an interactive online classification based on user labeling. The classification is obtained with a statistical learning method: kernels for similarity representation and SVM (Support Vector Machine) using binary user annotations. RETIN applies also an active learning strategy for proposing documents to the user for labeling. The system can deal with images, 3D objects and videos and other media can be added to. A graphical user interface allows easy browsing of different media, simple and user-friendly interaction and fast retrieval.

References

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Cited By

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  • (2019)A Semi-local Method for Image RetrievalIntelligent Systems Design and Applications10.1007/978-3-030-16660-1_16(165-172)Online publication date: 14-Apr-2019
  • (2018)Vector space model adaptation and pseudo relevance feedback for content-based image retrievalMultimedia Tools and Applications10.1007/s11042-017-4463-x77:5(5475-5501)Online publication date: 1-Mar-2018
  • (2012)Efficient image recovery using visual and semantic contents2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI)10.1109/CLEI.2012.6427147(1-10)Online publication date: Oct-2012
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cover image ACM Conferences
CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval
July 2007
655 pages
ISBN:9781595937339
DOI:10.1145/1282280
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 09 July 2007

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Author Tags

  1. content-based retrieval
  2. machine learning
  3. multimedia

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Cited By

View all
  • (2019)A Semi-local Method for Image RetrievalIntelligent Systems Design and Applications10.1007/978-3-030-16660-1_16(165-172)Online publication date: 14-Apr-2019
  • (2018)Vector space model adaptation and pseudo relevance feedback for content-based image retrievalMultimedia Tools and Applications10.1007/s11042-017-4463-x77:5(5475-5501)Online publication date: 1-Mar-2018
  • (2012)Efficient image recovery using visual and semantic contents2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI)10.1109/CLEI.2012.6427147(1-10)Online publication date: Oct-2012
  • (2011)Efficiency analysis in content based image retrieval using RDF annotationsProceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II10.1007/978-3-642-25330-0_25(285-296)Online publication date: 26-Nov-2011
  • (2010)Combining semantic and content based image retrieval in ORDBMSProceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II10.5555/1885375.1885381(44-53)Online publication date: 8-Sep-2010
  • (2010)Combining Semantic and Content Based Image Retrieval in ORDBMSKnowledge-Based and Intelligent Information and Engineering Systems10.1007/978-3-642-15390-7_5(44-53)Online publication date: 2010
  • (2009)Optimization on active learning strategy for object category retrievalProceedings of the 16th IEEE international conference on Image processing10.5555/1818719.1819243(1853-1856)Online publication date: 7-Nov-2009
  • (2009)Optimization on active learning strategy for object category retrieval2009 16th IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2009.5413554(1873-1876)Online publication date: Nov-2009

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