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CISST
2004
164views Hardware» more  CISST 2004»
13 years 9 months ago
Probabilistic Region Relevance Learning for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
Iker Gondra, Douglas R. Heisterkamp
SAC
2003
ACM
14 years 1 months ago
Improving Image Retrieval Effectiveness in Query-by-Example Environment
Query-by-example is the most popular query model for today’s image retrieval systems. A typical query image contains not only relevant objects (e.g., Eiffel Tower), but also ir...
Khanh Vu, Kien A. Hua, Ning Jiang
MSS
1999
IEEE
150views Hardware» more  MSS 1999»
14 years 2 days ago
Performance Benchmark Results for Automated Tape Library High Retrieval Rate Applications - Digital Check Image Retrievals
Benchmark tests have been designed and conducted for the purpose of evaluating the use of automated tape libraries in on-line digital check image retrieval applications. This type...
John Gniewek, George Davidson, Bowen Caldwell
SSIAI
2000
IEEE
14 years 6 days ago
Lower-Level and Higher-Level Approaches to Content-Based Image Retrieval
This paper describes a content-based image retrieval system that employs both higher-level and lower-level vision methodologies separately and in conjunction for the retrieval of ...
Qasim Iqbal, Jake K. Aggarwal
CVPR
2008
IEEE
14 years 9 months ago
A quasi-random sampling approach to image retrieval
In this paper, we present a novel approach to contentsbased image retrieval. The method hinges in the use of quasi-random sampling to retrieve those images in a database which are...
Jun Zhou, Antonio Robles-Kelly