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» Exploiting Unlabeled Data in Content-Based Image Retrieval
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ECML
2004
Springer
11 years 5 months ago
Exploiting Unlabeled Data in Content-Based Image Retrieval
Abstract. In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image ret...
Zhi-Hua Zhou, Ke-Jia Chen, Yuan Jiang
ICCV
2007
IEEE
12 years 1 months ago
Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval
Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of quest...
Hichem Sahbi, Jean-Yves Audibert, Renaud Keriven
CVPR
2006
IEEE
12 years 1 months ago
A Simple Bayesian Framework for Content-Based Image Retrieval
We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a userspecified ...
Katherine A. Heller, Zoubin Ghahramani
ICIP
1999
IEEE
12 years 1 months ago
In-Picture Search Algorithm for Content-Based Image Retrieval
Researchers are currently more interested in searching for fragments that are similar to a query, than a total data item that is similar to a query; the search interest is for &qu...
Punpiti Piamsa-nga, Nikitas A. Alexandridis, Sanan...
ICPR
2000
IEEE
12 years 20 days ago
Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective technique for adaptively computing local feature relevance for content-based image retrieval. It however becomes le...
Douglas R. Heisterkamp, Jing Peng, H. K. Dai
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