Structured sparse priors for image classification

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Umamahesh SrinivasTrac D Tran

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, 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.

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Citations

Nov 4, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yong LuoChao Xu
Mar 23, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jianwei ZhengWanliang Wang

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