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Associating low-level features with semantic concepts using video objects and relevance feedback
Sav, Sorin Vasile; O'Connor, Noel E.; Smeaton, Alan F.; Murphy, Noel
The holy grail of multimedia indexing and retrieval is developing algorithms capable of imitating human abilities in distinguishing and recognising semantic concepts within the content, so that retrieval can be based on ”real world” concepts that come naturally to users. In this paper, we discuss an approach to using segmented video objects as the midlevel connection between low-level features and semantic concept description. In this paper, we consider a video object as a particular instance of a semantic concept and we model the semantic concept as an average representation of its instances. A system supporting object-based search through a test corpus is presented that allows matching presegmented objects based on automatically extracted lowlevel features. In the system, relevance feedback is employed to drive the learning of the semantic model during a regular search process.
Keyword(s): Digital video; Information retrieval
Publication Date:
2005
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Sav, Sorin Vasile, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <https://orcid.org/0000-0002-4033-9135>, Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <https://orcid.org/0000-0003-1028-8389> and Murphy, Noel (2005) Associating low-level features with semantic concepts using video objects and relevance feedback. In: WIAMIS 2005 - 6th International Workshop on Image Analysis for Multimedia Interactive Services, 13-15 April 2005, Montreux, Switzerland.
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/403/1/wiamis_2005_2.pdf
First Indexed: 2009-11-05 02:00:39 Last Updated: 2019-02-09 07:03:45