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Related Experiment Video

Updated: Mar 28, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Parsing Based on Parselets: A Unified Deformable Mixture Model for Human Parsing.

Jian Dong, Qiang Chen, Zhongyang Huang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 17, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Parselets and a deformable mixture parsing model (DMPM) for direct human parsing, improving pixel-level accuracy without relying on human pose estimation.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Human parsing, essential for human-centric analysis, has traditionally relied on human pose estimation.
    • Previous methods face limitations in achieving optimal pixel-level parsing due to task inconsistencies.

    Purpose of the Study:

    • To directly address human parsing without intermediate tasks.
    • To introduce a novel Parselet representation and a Deformable Mixture Parsing Model (DMPM).

    Main Methods:

    • Utilizing Parselets, semantically meaningful segments from over-segmentation, as model building blocks.
    • Developing a DMPM with an "And-Or" structure to handle Parselet modalities and occlusion.
    • Incorporating visibility modeling at leaf nodes to address Parselet absence.

    Main Results:

    • The DMPM directly performs human parsing by optimizing Parselet configurations without pre-requisite tasks.
    • Hierarchical filtering ensures efficient processing.
    • Evaluations on a large-scale, high-resolution dataset and a benchmark dataset show encouraging performance.

    Conclusions:

    • The proposed Parselet representation and DMPM offer a direct and effective approach to human parsing.
    • The model demonstrates superior performance and efficiency in pixel-level semantic segmentation of humans.