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SIGIR
2008
ACM
13 years 7 months ago
Learning to reduce the semantic gap in web image retrieval and annotation
We study in this paper the problem of bridging the semantic gap between low-level image features and high-level semantic concepts, which is the key hindrance in content-based imag...
Changhu Wang, Lei Zhang 0001, Hong-Jiang Zhang
BDA
2007
13 years 9 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...
CLEF
2004
Springer
14 years 28 days ago
MSU at ImageCLEF: Cross Language and Interactive Image Retrieval
Abstract. In this report, we describe our studies with cross language and interactive image retrieval in ImageCLEF 2004. Typical cross language retrieval requires special linguisti...
Vineet Bansal, Chen Zhang, Joyce Y. Chai, Rong Jin
CVPR
2004
IEEE
14 years 9 months ago
Object-Based Image Retrieval Using the Statistical Structure of Images
We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems in...
Derek Hoiem, Rahul Sukthankar, Henry Schneiderman,...
CVPR
2001
IEEE
14 years 9 months ago
Optimal Adaptive Learning for Image Retrieval
Learning-enhanced relevance feedback is one of the most promising and active research directions in recent year's content-based image retrieval. However, the existing approac...
Tao Wang, Yong Rui, Shi-Min Hu