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ICCV
2007
IEEE
14 years 2 days ago
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which ha...
Ondrej Chum, James Philbin, Josef Sivic, Michael I...
MLDM
1999
Springer
13 years 10 months ago
Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval
In large content-based image database applications, e cient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques f...
Irwin King, Tak-Kan Lau
CIVR
2003
Springer
133views Image Analysis» more  CIVR 2003»
13 years 11 months ago
A Closer Look at Boosted Image Retrieval
Margin-maximizing techniques such as boosting have been generating excitement in machine learning circles for several years now. Although these techniques offer significant impro...
Nicholas R. Howe
MM
2009
ACM
269views Multimedia» more  MM 2009»
14 years 8 days ago
Semi-supervised topic modeling for image annotation
We propose a novel technique for semi-supervised image annotation which introduces a harmonic regularizer based on the graph Laplacian of the data into the probabilistic semantic ...
Yuanlong Shao, Yuan Zhou, Xiaofei He, Deng Cai, Hu...
MIR
2005
ACM
140views Multimedia» more  MIR 2005»
13 years 11 months ago
Multiple random walk and its application in content-based image retrieval
In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by mea...
Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying M...