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ICTIR
2009
Springer
14 years 5 months ago
A Four-Factor User Interaction Model for Content-Based Image Retrieval
In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques i...
Haiming Liu 0002, Victoria S. Uren, Dawei Song, St...
ICDIM
2006
IEEE
14 years 4 months ago
Model-Driven Development of Content-Based Image Retrieval Systems
: Generic systems for content-based image retrieval (CBIR), such as QBIC [7] cannot be used to solve domain-specific image retrieval problems, as for example, the identification of...
Temenushka Ignatova
CIVR
2004
Springer
213views Image Analysis» more  CIVR 2004»
14 years 3 months ago
Evaluation of Texture Features for Content-Based Image Retrieval
Abstract. We have carried out a detailed evaluation of the use of texture features in a query-by-example approach to image retrieval. We used 3 radically different texture feature...
Peter Howarth, Stefan M. Rüger
VIP
2003
13 years 11 months ago
Relevance Feedback for Content-Based Image Retrieval Using Bayesian Network
Relevance feedback is a powerful query modification technique in the field of content-based image retrieval. The key issue in relevance feedback is how to effectively utilize the ...
Jing Xin, Jesse S. Jin
CLEF
2005
Springer
14 years 4 months ago
Content-Based Retrieval of Medical Images by Combining Global Features
A combination of several classifiers using global features for the content description of medical images is proposed. Beside well known texture histogram features, downscaled repr...
Mark Oliver Güld, Christian Thies, Benedikt F...