2009-04-10 : New paper on feature learning available

Learning Invariant Features through Topographic Filter Maps, to appear in CVPR 2009. The method describes a method to learn SIFT-like locally-invariant feature detectors from data. The method minimizes a reconstruction criterion under a “block sparsity constraint”, which minimizes the number of “pools” of features that are active. filters whose outputs are grouped in a pool end up detecting similar features, and the pool outputs can be interpreted as complex-cell like locally-invariant features

news/newsitems/2009-04-10.txt · Last modified: 2009/10/23 22:37 by koray