title: Structured sparse priors for image classification.
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.
authors.forename: Umamahesh
authors.initials: U
authors.lastname: Srinivas
authors.forename: Yuanming
authors.initials: Y
authors.lastname: Suo
authors.forename: Minh
authors.initials: M
authors.lastname: Dao
authors.forename: Vishal
authors.initials: V
authors.lastname: Monga
authors.forename: Trac D
authors.initials: TD
authors.lastname: Tran
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D044962:Data Compression / Q000379:methods; D003627:Data Interpretation, Statistical; D005145:Face / Q000033:anatomy & histology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D013382:Subtraction Technique