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ICCV
2011
IEEE
14 years 4 months ago
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers ...
Matthew D. Zeiler, Graham W. Taylor, Rob Fergus
ICCV
2011
IEEE
14 years 4 months ago
Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features
This paper addresses scene understanding in the context of a moving camera, integrating semantic reasoning ideas from monocular vision with 3D information available through struct...
Alex Flint, David Murray, Ian Reid
ICDAR
2011
IEEE
14 years 4 months ago
Segmentation and Normalisation in Grapheme Codebooks
Abstract—The grapheme codebook is a high-performing technique for offline writer identification. This paper considers whether the de facto standards for initial grapheme extrac...
Tara Gilliam, Richard C. Wilson, John A. Clark
IDEAS
2007
IEEE
128views Database» more  IDEAS 2007»
15 years 10 months ago
Streaming Random Forests
Many recent applications deal with data streams, conceptually endless sequences of data records, often arriving at high flow rates. Standard data-mining techniques typically assu...
Hanady Abdulsalam, David B. Skillicorn, Patrick Ma...
CORR
2012
Springer
183views Education» more  CORR 2012»
14 years 4 days ago
Learning Determinantal Point Processes
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
Alex Kulesza, Ben Taskar
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