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CVPR
2008
IEEE
10 years 7 months ago
Lost in quantization: Improving particular object retrieval in large scale image databases
The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local ...
James Philbin, Ondrej Chum, Michael Isard, Josef S...
CVPR
2009
IEEE
1289views Computer Vision» more  CVPR 2009»
11 years 28 days ago
Bundling Features for Large Scale Partial-Duplicate Web Image Search
In state-of-the-art image retrieval systems, an image is represented by a bag of visual words obtained by quantizing high-dimensional local image descriptors, and scalable schem...
Zhong Wu (Tsinghua University), Qifa Ke (Microsoft...
ICCV
2007
IEEE
9 years 12 months 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...
CVPR
2006
IEEE
9 years 11 months ago
Scalable Recognition with a Vocabulary Tree
A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers...
David Nistér, Henrik Stewénius
CORR
2016
Springer
59views Education» more  CORR 2016»
4 years 2 months ago
Group Invariant Deep Representations for Image Instance Retrieval
Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in...
Olivier Morère, Antoine Veillard, Jie Lin, ...
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