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» Learning the Relative Importance of Features in Image Data
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CVPR
2010
IEEE
14 years 5 months ago
Learning Mid-Level Features For Recognition
Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of interme...
Y-Lan Boureau, Francis Bach, Yann LeCun, Jean Ponc...
CVPR
2010
IEEE
14 years 5 months ago
Unsupervised Learning of Invariant Features Using Video
We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without hu...
David Stavens, Sebastian Thrun
CVPR
2011
IEEE
13 years 4 months ago
Hierarchical Semantic Indexing for Large Scale Image Retrieval
This paper addresses the problem of similar image retrieval, especially in the setting of large-scale datasets with millions to billions of images. The core novel contribution is ...
Jia Deng, Alexander Berg, Li Fei-Fei
JFR
2006
75views more  JFR 2006»
13 years 8 months ago
Topological map learning from outdoor image sequences
We propose an approach to building topological maps of environments based on image sequences. The central idea is to use manifold constraints to find representative feature protot...
Xuming He, Richard S. Zemel, Volodymyr Mnih
AIED
2009
Springer
14 years 3 months ago
Intelligent Support for Inquiry Learning from Images: A Learning Scenario and Tool
Inquiry learning involves the learner acquiring new concepts and skills by means of carrying out an investigation. Some previous studies have looked into how these learning activit...
Paul Mulholland, Zdenek Zdráhal, Jan Abraha...