Effective and efficient subjective testing of texture similarity metrics

Jana Zujovic, Thrasyvoulos N. Pappas*, David L. Neuhoff, René Van Egmond, Huib De Ridder

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The development and testing of objective texture similarity metrics that agree with human judgments of texture similarity require, in general, extensive subjective tests. The effectiveness and efficiency of such tests depend on a careful analysis of the abilities of human perception and the application requirements. The focus of this paper is on defining performance requirements and testing procedures for objective texture similarity metrics. We identify three operating domains for evaluating the performance of a similarity metric: the ability to retrieve "identical" textures; the top of the similarity scale, where a monotonic relationship between metric values and subjective scores is desired; and the ability to distinguish between perceptually similar and dissimilar textures. Each domain has different performance goals and requires different testing procedures. For the third domain, we propose ViSiProG, a new Visual Similarity by Progressive Grouping procedure for conducting subjective experiments that organizes a texture database into clusters of visually similar images. The grouping is based on visual blending and greatly simplifies labeling image pairs as similar or dissimilar. ViSiProG collects subjective data in an efficient and effective manner, so that a relatively large database of textures can be accommodated. Experimental results and comparisons with structural texture similarity metrics demonstrate both the effectiveness of the proposed subjective testing procedure and the performance of the metrics.

Original languageEnglish (US)
Pages (from-to)329-342
Number of pages14
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number2
StatePublished - 2015

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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