Perceptual similarity metrics for retrieval of natural textures

Jana Zujovic*, Thrasyvoulos N Pappas, David L. Neuhoff

*Corresponding author for this work

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

10 Scopus citations

Abstract

We investigate perceptual similarity metrics for the content-based retrieval of natural textures. The goal is to find perceptually similar textures that may have significant differences on a point-by-point basis. The evaluation of such metrics typically requires extensive and cumbersome subjective tests. The focus of this paper is on the recovery of textures that are "identical" to the query texture, in the sense that they are pieces of the same texture. This is important in content-based image retrieval (CBIR), where one may want to find images that contain a particular texture, as well as in some near-threshold coding applications. The advantage of evaluating metric performance in the context of retrieving identical textures is that the ground truth is known, and therefore no subjective tests are required. We can thus compare the performance of different metrics on large sets of textures, and derive meaningful statistical results. We evaluate the performance of a recently proposed structural texture similarity metric on grayscale textures, and compare it to that of PSNR, as well as space domain and complex wavelet structural similarity metrics. Experimental results with a database of 748 distinct texture images, indicate that the new metric outperforms the other metrics in the retrieval of identical textures, according to a number of standard statistical measures.

Original languageEnglish (US)
Title of host publication2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
DOIs
StatePublished - Dec 1 2009
Event2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09 - Rio De Janeiro, Brazil
Duration: Oct 5 2009Oct 7 2009

Publication series

Name2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09

Other

Other2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
CountryBrazil
CityRio De Janeiro
Period10/5/0910/7/09

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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