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Related Experiment Video

Updated: Apr 3, 2026

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
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Multi-Graph Matching via Affinity Optimization with Graduated Consistency Regularization.

Junchi Yan, Minsu Cho, Hongyuan Zha

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 16, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces novel composition-based methods for multi-graph matching, effectively integrating local affinities and global consistency. The approach reduces errors by gradually infusing matching consistency, improving common node correspondence identification.

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    Last Updated: Apr 3, 2026

    Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
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    Published on: February 27, 2020

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    Area of Science:

    • Computer Science
    • Graph Theory
    • Computer Vision

    Background:

    • Multi-graph matching seeks common node correspondences across related graph structures.
    • Existing methods often separate or prematurely enforce matching consistency, leading to error propagation.

    Purpose of the Study:

    • To develop a novel approach for multi-graph matching that integrates local pairwise affinities and global matching consistency.
    • To improve the accuracy and robustness of identifying common node correspondences in multiple graphs.

    Main Methods:

    • Proposed composition-based multi-graph matching methods.
    • Optimized affinity scores while gradually incorporating matching consistency.
    • Developed two mechanisms to distinguish common inliers from outliers.

    Main Results:

    • Demonstrated the competency of the proposed algorithms on synthetic and real image datasets.
    • Showcased effective integration of affinity and consistency for improved matching.
    • Successfully elicited common inliers against outliers.

    Conclusions:

    • The proposed composition-based approach offers a more effective strategy for multi-graph matching.
    • Gradual infusion of consistency acts as a powerful regularizer, mitigating errors.
    • The methods show significant potential for applications requiring robust graph structure comparison.