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 non-negative 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 are discriminatory and highly effective for classification.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Sparse image coding using a 3D non-negative tensor factorization


    Contributors:
    Hazan, T. (author) / Polak, S. (author) / Shashua, A. (author)


    Publication date :

    2005-01-01


    Size :

    508363 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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

    Hazan, T. / Polak, S. / Shashua, A. et al. | British Library Conference Proceedings | 2005


    Controlling Sparseness in Non-negative Tensor Factorization

    Heiler, M. / Schnorr, C. | British Library Conference Proceedings | 2006



    Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization

    Shashua, A. / Zass, R. / Hazan, T. | British Library Conference Proceedings | 2006


    Separation of reflection components by sparse non-negative matrix factorization

    Akashi, Yasushi / Okatani, Takayuki | British Library Online Contents | 2016