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» Mean Shift Feature Space Warping For Relevance Feedback
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ICIP
2009
IEEE
14 years 6 months ago
Mean Shift Feature Space Warping For Relevance Feedback
Relevance feedback has been taken as an essential tool to enhance content-based information retrieval systems by keeping the user in the retrieval loop. Among the fundamental rele...
MLDM
2001
Springer
13 years 9 months ago
Adaptive Query Shifting for Content-Based Image Retrieval
: Despite the efforts to reduce the semantic gap between user perception of similarity and featurebased representation of images, user interaction is essential to improve retrieval...
Giorgio Giacinto, Fabio Roli, Giorgio Fumera
MIR
2004
ACM
171views Multimedia» more  MIR 2004»
13 years 10 months ago
Mean version space: a new active learning method for content-based image retrieval
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
Jingrui He, Hanghang Tong, Mingjing Li, HongJiang ...
BDA
2007
13 years 6 months ago
Hyperplane Queries in a Feature-Space M-tree for Speeding up Active Learning
In content-based retrieval, relevance feedback (RF) is a noticeable method for reducing the “semantic gap” between the low-level features describing the content and the usually...
Michel Crucianu, Daniel Estevez, Vincent Oria, Jea...
ICASSP
2007
IEEE
13 years 11 months ago
A Radial Basis Function and Semantic Learning Space Based Composite Learning Approach to Image Retrieval
This paper introduces a composite learning approach for image retrieval with relevance feedback. The proposed system combines the radial basis function (RBF) based lowlevel learni...
Konstantin Shkurko, Xiaojun Qi