Structured sparse priors for image classification

Umamahesh Srinivas, Yuanming Suo, Minh Dao, Vishal Monga, Trac D. Tran

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

    4 Scopus citations

    Abstract

    Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, the Laplacian being the most common such choice (leading to l1-norm minimization). The recent seminal contribution by Wright et al. exploits the discriminative capability of sparse representations for image classification, specifically face recognition. Their approach employs the analytical framework of CS with class-specific dictionaries. Our contribution is a logical extension of these ideas into structured sparsity for classification. We use class-specific dictionaries in conjunction with discriminative class-specific priors, specifically the spike-and-slab prior widely applied in Bayesian regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes.

    Original languageEnglish (US)
    Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
    Pages3211-3215
    Number of pages5
    DOIs
    StatePublished - Dec 1 2013
    Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
    Duration: Sep 15 2013Sep 18 2013

    Publication series

    Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

    Other

    Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
    CountryAustralia
    CityMelbourne, VIC
    Period9/15/139/18/13

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition

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