Structured Sparse Priors for Image Classification

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

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


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, with the Laplacian being the most common such choice (leading to l1-norm minimization). Recent work has exploited the discriminative capability of sparse representations for image classification by employing class-specific dictionaries in the CS framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We introduce the notion of discriminative class-specific priors in conjunction with class specific dictionaries, specifically the spike-and-slab prior widely applied in Bayesian sparse 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. We demonstrate this practical benefit of our approach in important applications, such as face recognition and object categorization.

Original languageEnglish (US)
Article number7055925
Pages (from-to)1763-1776
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number6
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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