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» Multiclass Object Recognition with Sparse, Localized Feature...
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126
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ICPR
2006
IEEE
16 years 3 months ago
Rotation-Invariant Neoperceptron
Approaches based on local features and descriptors are increasingly used for the task of object recognition due to their robustness with regard to occlusions and geometrical defor...
Beat Fasel, Daniel Gatica-Perez
141
Voted
KDD
2006
ACM
213views Data Mining» more  KDD 2006»
16 years 3 months ago
Learning sparse metrics via linear programming
Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficie...
Glenn Fung, Rómer Rosales
ICCV
2009
IEEE
16 years 7 months ago
What is the Best Multi-Stage Architecture for Object Recognition?
In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a non-linear transformation, and some sort of feature pooling layer...
Kevin Jarrett, Koray Kavukcuoglu, Marc’Aurelio R...
145
Voted
ICML
2010
IEEE
15 years 3 months ago
A Theoretical Analysis of Feature Pooling in Visual Recognition
Many modern visual recognition algorithms incorporate a step of spatial `pooling', where the outputs of several nearby feature detectors are combined into a local or global `...
Y-Lan Boureau, Jean Ponce, Yann LeCun
241
Voted
ECCV
2008
Springer
15 years 4 months ago
SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor
Extraction and matching of discriminative feature points in images is an important problem in computer vision with applications in image classification, object recognition, mosaici...
Raj Gupta, Anurag Mittal