Proceedings chapter

Semantic clustering of images using patterns of relevance feedback

Presented at London (UK), Jun 18-20
Publication date2008

User-supplied data such as browsing logs, click-through data, and relevance feedback judgements are an important source of knowledge during semantic indexing of documents such as images and video. Low-level indexing and abstraction methods are limited in the manner with which semantic data can be dealt. In this paper and in the context of this semantic data, we apply latent semantic analysis on two forms of user-supplied data, real-world and artificially generated relevance feedback judgements in order to examine the validity of using artificially generated interaction data for the study of semantic image clustering.

  • Image clustering
  • latent semantic analysis
  • longterm learning
  • relevance feedback
Citation (ISO format)
MORRISON, Donn Alexander, MARCHAND-MAILLET, Stéphane, BRUNO, Eric. Semantic clustering of images using patterns of relevance feedback. In: 6th International Workshop on Content-based Multimedia Indexing (CBMI08). London (UK). [s.l.] : IEEE, 2008. p. 323–329. doi: 10.1109/CBMI.2008.4564964
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Proceedings chapter (Published version)

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