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Related Experiment Video

Updated: Mar 22, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

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Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs.

Mohsen Zand, Shyamala Doraisamy, Alfian Abdul Halin

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 13, 2016
    PubMed
    Summary
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    This study introduces an ontology-based semantic image segmentation (OBSIS) approach. OBSIS improves image segmentation and object detection by transforming visual data into a semantic space and using an ontology model for inference.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Semantic image segmentation partitions images into labeled regions.
    • Current methods often struggle with feature distinctiveness and contextual integration.
    • Existing approaches using conditional random fields (CRFs) can degrade performance due to feature limitations.

    Purpose of the Study:

    • To propose an ontology-based semantic image segmentation (OBSIS) approach.
    • To jointly model image segmentation and object detection.
    • To enhance image understanding by mimicking human cognitive processes.

    Main Methods:

    • Utilized a Dirichlet process mixture model to create an intermediate semantic space from low-level visual features.
    • Employed multiple CRFs for individually weighted and independently learned feature extraction.

    Related Experiment Videos

    Last Updated: Mar 22, 2026

    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
    09:21

    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

    Published on: February 18, 2015

    12.7K
  • Integrated an ontology model for object inference, reducing segmentation to a classification task.

    • Main Results:

    • Transformed low-level visual space into a high-level semantic space, reducing feature dimensionality.
    • Achieved promising results on MSRC-21 and PASCAL VOC'2010 datasets.
    • Demonstrated improved performance in semantic image segmentation and object detection.

    Conclusions:

    • The proposed OBSIS approach offers a novel method for semantic image segmentation.
    • Jointly modeling segmentation and detection with an ontology enhances image understanding.
    • OBSIS shows potential for more accurate and human-like image interpretation.