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ICMCS
2000
IEEE
170views Multimedia» more  ICMCS 2000»
13 years 12 months ago
Update Relevant Image Weights for Content-Based Image Retrieval using Support Vector Machines
Relevance feedback [1] has been a powerful tool for interactive Content-Based Image Retrieval (CBIR). During the retrieval process, the user selects the most relevant images and p...
Qi Tian, Pengyu Hong, Thomas S. Huang
JMLR
2010
141views more  JMLR 2010»
13 years 2 months ago
Pinview: Implicit Feedback in Content-Based Image Retrieval
This paper describes Pinview, a content-based image retrieval system that exploits implicit relevance feedback during a search session. Pinview contains several novel methods that...
Peter Auer, Zakria Hussain, Samuel Kaski, Arto Kla...
AIRS
2009
Springer
13 years 11 months ago
Enabling Effective User Interactions in Content-Based Image Retrieval
Abstract. This paper presents an interactive content-based image retrieval framework--uInteract, for delivering a novel four-factor user interaction model visually. The four-factor...
Haiming Liu 0002, Srdan Zagorac, Victoria S. Uren,...
RIAO
2000
13 years 8 months ago
Image indexation and content based search using pre-attentive similarities
We introduce in this paper the general architecture of an image search engine based on pre-attentive similarities. The components of this system are presented and some of them are...
Alexander Heinrichs, Dimitri Koubaroulis, Barbara ...
VISUAL
2000
Springer
13 years 11 months ago
Content-Based Image Retrieval by Relevance Feedback
Relevance feedback is a powerful technique for content-based image retrieval. Many parameter estimation approaches have been proposed for relevance feedback. However, most of them ...
Zhong Jin, Irwin King, Xuequn Li