Sparsity constrained estimation in image processing and computer vision

Vishal Monga, Hojjat Seyed Mousavi, Umamahesh Srinivas

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Over the past decade, sparsity has emerged as a dominant theme in signal processing and big data applications. In this chapter, we formulate and solve new flavors of sparsity-constrained optimization problems built on the family of spike-and-slab priors. First, we develop an efficient Iterative Convex Refinement solution to the hard non-convex problem of Bayesian signal recovery under sparsity-inducing spike-and-slab priors. We also offer a Bayesian perspective on sparse representation-based classification via the introduction of class-specific priors. This formulation represents a consummation of ideas developed for model-based compressive sensing into a general framework for sparse model-based classification.

    Original languageEnglish (US)
    Title of host publicationHandbook of Convex Optimization Methods in Imaging Science
    PublisherSpringer International Publishing
    Pages177-206
    Number of pages30
    ISBN (Electronic)9783319616094
    ISBN (Print)9783319616087
    DOIs
    StatePublished - Jan 1 2017

    All Science Journal Classification (ASJC) codes

    • Computer Science(all)
    • Engineering(all)

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