Associating low-level features with semantic concepts using video objects and relevance feedback
Sav, Sorin and O'Connor, Noel E. and Smeaton, Alan F. 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.
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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.
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