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CISST
2004
164views Hardware» more  CISST 2004»
11 years 1 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
ICPR
2000
IEEE
12 years 24 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
MLDM
2001
Springer
11 years 4 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
2005
ACM
133views Multimedia» more  MIR 2005»
11 years 5 months ago
Probabilistic web image gathering
We propose a new method for automated large scale gathering of Web images relevant to speciļ¬ed concepts. Our main goal is to build a knowledge base associated with as many conce...
Keiji Yanai, Kobus Barnard
CIVR
2005
Springer
123views Image Analysis» more  CIVR 2005»
11 years 5 months ago
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
Chengcui Zhang, Xin Chen
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