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 Experiment Video

Updated: Jun 5, 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

Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis.

Roman Sandler, Michael Lindenbaum

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 26, 2011
    PubMed
    Summary

    Two new nonnegative matrix factorization (NMF) algorithms minimize Earth Mover's Distance (EMD) error for computer vision tasks. These EMD NMF methods offer advantages over traditional L2-NMF for image segmentation, classification, and recognition.

    Related Concept Videos

    You might also read

    Related Articles

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

    Sort by
    Same author

    Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array.

    Data in brief·2025
    Same author

    The cues in “dependent multiple cue integration for robust tracking” are independent.

    IEEE transactions on pattern analysis and machine intelligence·2014
    Same author

    Blind adaptive sampling of images.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2011
    Same author

    Dense mirroring surface recovery from 1D homographies and sparse correspondences.

    IEEE transactions on pattern analysis and machine intelligence·2011
    Same author

    Esaliency (extended saliency): meaningful attention using stochastic image modeling.

    IEEE transactions on pattern analysis and machine intelligence·2010
    Same author

    Tracking by affine kernel transformations using color and boundary cues.

    IEEE transactions on pattern analysis and machine intelligence·2008

    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Analysis

    Background:

    • Nonnegative matrix factorization (NMF) is crucial for data approximation in computer vision.
    • Existing NMF methods often minimize L2 or KL distances, yielding specific matrix properties.
    • The limitations of current NMF distance metrics in certain applications motivate new approaches.

    Purpose of the Study:

    • To introduce two novel NMF algorithms that minimize Earth Mover's Distance (EMD) error.
    • To explore the application of EMD-based NMF in computer vision tasks.
    • To demonstrate the convergence and practical utility of the proposed EMD NMF algorithms.

    Main Methods:

    • Developed two iterative NMF algorithms: EMD NMF and bilateral EMD NMF.
    • Utilized linear programming methods as the basis for the iterative algorithms.

    More Related Videos

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC
    06:17

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC

    Published on: November 7, 2025

    Related Experiment Videos

    Last Updated: Jun 5, 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

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC
    06:17

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC

    Published on: November 7, 2025

  • Analyzed convergence properties and addressed numerical challenges with efficient approximations.

    • Main Results:

    • The proposed EMD NMF algorithms produce distinct matrix factorizations compared to L2-NMF.
    • Demonstrated the effectiveness of EMD NMF in texture classification and face recognition benchmarks.
    • Achieved the first instance of NMF-based image segmentation using the new methods.

    Conclusions:

    • EMD NMF offers a valuable alternative to traditional NMF methods for specific computer vision problems.
    • The new algorithms show significant advantages in image segmentation, texture classification, and face recognition.
    • The convergence proofs and practical approximations support the viability of EMD NMF.