Non-greedy adaptive compressive imaging: A face recognition example

James L. Huang, Mark A Neifeld, Amit Ashok

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

Abstract

We present a non-greedy adaptive compressive measurement design for application to an M-class recognition task. Unlike a greedy strategy which sequentially optimizes the immediate performance conditioned on previous measurement, a non-greedy adaptive design determines the optimal measurement vector by maximizing the expected final performance. Gaussian class conditional densities are used to model the variety of object realization for each hypothesis. The simulation results demonstrate that non-greedy adaptive design significantly reduces the probability of recognition error from greedy adaptive and various static measurement designs by 22% and 33%, respectively.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages762-764
Number of pages3
ISBN (Print)9781479923908
DOIs
Publication statusPublished - 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

    Fingerprint

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

Cite this

Huang, J. L., Neifeld, M. A., & Ashok, A. (2013). Non-greedy adaptive compressive imaging: A face recognition example. In Conference Record - Asilomar Conference on Signals, Systems and Computers (pp. 762-764). [6810387] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810387