Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Training and Testing Texture Similarity Metrics for Structurally Lossless Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

Pattern-Based Reconstruction of K-Level Images From Cutsets.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Human Skin Gloss Perception Based on Texture Statistics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same author

Hierarchical Lossy Bilevel Image Compression Based on Cutset Sampling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020
Same author

Similarity of Scenic Bilevel Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2017
Same author

Effective and efficient subjective testing of texture similarity metrics.

Journal of the Optical Society of America. A, Optics, image science, and vision·2015

Related Experiment Video

Updated: May 13, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Structural texture similarity metrics for image analysis and retrieval.

Jana Zujovic1, Thrasyvoulos N Pappas, David L Neuhoff

  • 1Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA. jana.ehmann@huawei.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 14, 2013
PubMed
Summary

New texture similarity metrics capture human perception, outperforming existing methods in identifying identical textures. These metrics use local image statistics for more accurate texture matching and retrieval.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Related Experiment Videos

Last Updated: May 13, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Area of Science:

  • Computer Vision
  • Image Processing
  • Perceptual Science

Background:

  • Traditional image metrics like PSNR and SSIM often fail to capture human perception of texture similarity.
  • Existing texture analysis methods may not adequately address the stochastic nature of real-world textures.

Purpose of the Study:

  • To develop novel texture similarity metrics that align with human visual perception.
  • To create metrics robust to point-by-point deviations while preserving perceived texture identity.
  • To enable efficient and large-scale evaluation of texture retrieval performance.

Main Methods:

  • Utilizing local image statistics and steerable filter decomposition.
  • Incorporating subband statistics computed globally or via sliding windows.
  • Evaluating metrics in a "known-item search" task for direct comparison with human performance.

Main Results:

  • The proposed metrics demonstrate superior performance compared to PSNR, SSIM, and state-of-the-art texture classification metrics.
  • Metrics effectively identify perceptually identical textures despite local variations.
  • Systematic tests confirm the efficacy of the new metrics in large-scale database retrieval.

Conclusions:

  • The developed metrics offer a more perceptually relevant approach to texture similarity assessment.
  • These metrics advance texture analysis-synthesis and image retrieval applications.
  • The findings provide a robust framework for evaluating texture similarity aligned with human judgment.