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ICCV
2005
IEEE

Sparse Image Coding Using a 3D Non-Negative Tensor Factorization

10 years 5 months ago
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
We introduce an algorithm for a non-negative 3D tensor factorization for the purpose of establishing a local parts feature decomposition from an object class of images. In the past such a decomposition was obtained using nonnegative matrix factorization (NMF) where images were vectorized before being factored by NMF. A tensor factorization (NTF) on the other hand preserves the 2D representations of images and provides a unique factorization (unlike NMF which is not unique). The resulting "factors" from the NTF factorization are both sparse (like with NMF) but also separable allowing efficient convolution with the test image. Results show a superior decomposition to what an NMF can provide on all fronts -- degree of sparsity, lack of ghost residue due to invariant parts and efficiency of coding of around an order of magnitude better. Experiments on using the local parts decomposition for face detection using SVM and Adaboost classifiers demonstrate that the recovered features...
Tamir Hazan, Simon Polak, Amnon Shashua
Added 15 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Tamir Hazan, Simon Polak, Amnon Shashua
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