How well do computational features perceptually rank textures? A comparative evaluation

Xinghui Dong, Thomas S. Methven, Mike J. Chantler

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

11 Citations (Scopus)

Abstract

Inspired by studies [4, 23, 40] which compared rankings obtained by search engines and human observers, in this paper we compare texture rankings derived by 51 sets of computational features against perceptual texture rankings obtained from a free-grouping experiment with 30 human observers, using a unify evaluation framework. Experimental results show that the MRSAR [37], VZNEIGHBORHOOD [62], LBPHF [2] and LBPBASIC [3] feature sets perform better than their counterparts. However, none of those feature sets are ideal. The best average G and M measures (measures of ranking accuracy from 0 to 1) [15, 5] obtained are 0.36 and 0.25 respectively. We suggest that this poor performance may be due to the small local neighborhood used to calculate higher-order features which cannot capture the long-range interactions that humans have been shown to exploit [14, 16, 49, 56]. Copyright is held by the owner/author(s).

Original languageEnglish
Title of host publicationICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014
PublisherAssociation for Computing Machinery
Pages281-288
Number of pages8
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 4th ACM International Conference on Multimedia Retrieval - Glasgow, United Kingdom
Duration: 1 Apr 20144 Apr 2014

Conference

Conference2014 4th ACM International Conference on Multimedia Retrieval
Abbreviated titleICMR 2014
CountryUnited Kingdom
CityGlasgow
Period1/04/144/04/14

Keywords

  • Computational features
  • Evaluation
  • Perceptual texture ranking
  • Texture ranking
  • Texture retrieval
  • Texture similarity

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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