A classification centric quantizer for efficient encoding of predictive feature errors

Scott Deeann Chen, Pierre Moulin

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

Abstract

We design a joint compression and classification system that optimizes visual fidelity and classification accuracy under a bit rate constraint. We propose a classification centric quantizer (CCQ) whose parameters are learned from labeled training data. We apply and evaluate the CCQ on a scene classification problem and compare the results to previous work.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages2098-2102
Number of pages5
ISBN (Electronic)9781479982974
DOIs
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2015-April
ISSN (Print)1058-6393

Other

Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/2/1411/5/14

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Deeann Chen, S., & Moulin, P. (2015). A classification centric quantizer for efficient encoding of predictive feature errors. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 2098-2102). [7094844] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094844