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Improved entropy of primitive for visual information estimation

  • Shurun Wang
  • , Zhenghui Zhao
  • , Xiang Zhang
  • , Jian Zhang
  • , Shiqi Wang
  • , Siwei Ma
  • , Wen Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ2 norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences.

Original languageEnglish
Title of host publicationVCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053162
DOIs
StatePublished - 4 Jan 2017
Externally publishedYes
Event2016 IEEE Visual Communication and Image Processing, VCIP 2016 - Chengdu, China
Duration: 27 Nov 201630 Nov 2016

Publication series

NameVCIP 2016 - 30th Anniversary of Visual Communication and Image Processing

Conference

Conference2016 IEEE Visual Communication and Image Processing, VCIP 2016
Country/TerritoryChina
CityChengdu
Period27/11/1630/11/16

Keywords

  • Entropy of primitive
  • orthogonal matching pursuit
  • sparse representation
  • visual information estimation

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