Learning a low-rank shared dictionary for object classification

Tiep H. Vu, Vishal Monga

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    13 Scopus citations

    Abstract

    Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. Inspired by this observation, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e. claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Further, we propose a new fast and accurate algorithm to solve the sparse coding problems in the learning step, accelerating its convergence. The said algorithm could also be applied to FDDL and its extensions. Experimental results on widely used image databases establish the advantages of our method over state-of-the-art dictionary learning methods.

    Original languageEnglish (US)
    Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
    PublisherIEEE Computer Society
    Pages4428-4432
    Number of pages5
    ISBN (Electronic)9781467399616
    DOIs
    StatePublished - Aug 3 2016
    Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
    Duration: Sep 25 2016Sep 28 2016

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    Volume2016-August
    ISSN (Print)1522-4880

    Other

    Other23rd IEEE International Conference on Image Processing, ICIP 2016
    CountryUnited States
    CityPhoenix
    Period9/25/169/28/16

    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

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  • Cite this

    Vu, T. H., & Monga, V. (2016). Learning a low-rank shared dictionary for object classification. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (pp. 4428-4432). [7533197] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7533197