Mining visual complexity of images based on an enhanced feature space representation

Abdullah M. Iliyasu, Awad Kh Al-Asmari, Mohamed A. Abdelwahab, Ahmed S. Salama, Mohamed A. Al-Qodah, Asif R. Khan, Phuc Q. Le, Fei Yan

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

2 Citations (Scopus)

Abstract

An enhanced feature space to represent visual complexity of images, as would the HVS, is presented. Specifically, the ratio between the coherent and incoherent pixels in an image was used as a measure of the chromatic contributions to the visual complexity of an image. Similarly, the contrast, energy, entropy and homogeneity were modelled as the textural attributes of an image's visual complexity. Integrated into the SND feature space, these new (chromatic and textural) features facilitate a better and enhanced representation of visual complexity. Using the Corel 1000A dataset to validate the veracity of the proposal, the enhanced visual complexity space, the SND+ space, improves the capability to better represent visual complexity by a 16.7% increase in the exact correlation with a subjective (human) evaluation of the same dataset over the original SND space. Pursued further, the effective representation of visual complexity would have profound impacts in many areas of image processing and computer vision.

Original languageEnglish
Title of host publication2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings
PublisherIEEE Computer Society
Pages65-70
Number of pages6
ISBN (Print)9781467345439
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Funchal, Madeira, Portugal
Duration: Sep 16 2013Sep 18 2013

Publication series

Name2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings

Conference

Conference2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013
Country/TerritoryPortugal
CityFunchal, Madeira
Period9/16/139/18/13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Signal Processing

Fingerprint

Dive into the research topics of 'Mining visual complexity of images based on an enhanced feature space representation'. Together they form a unique fingerprint.

Cite this