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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Multiple Riemannian Kernel Hashing for Large-Scale Image Set Classification and Retrieval.

Xiaobo Shen, Wei Wu, Xiaxin Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 2, 2024
    PubMed
    Summary

    This study introduces Multiple Riemannian Kernel Hashing (MRKH) for large-scale image set analysis. MRKH efficiently models complex image sets, improving classification and retrieval accuracy.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Science

    Background:

    • Conventional image set methods struggle with large datasets, facing challenges in modeling complexity and efficiency.
    • Existing techniques are often limited to small or medium-sized image set applications.

    Purpose of the Study:

    • To develop a novel method for effective and efficient image set representation, particularly for large-scale applications.
    • To address the limitations of conventional methods in handling complex image sets and computational demands.

    Main Methods:

    • Proposes Multiple Riemannian Kernel Hashing (MRKH), utilizing Riemannian manifolds and hashing for image set representation.
    • Employs a multiple kernel learning framework to combine statistics from heterogeneous Riemannian manifolds.
    • Generates hash codes instead of continuous features for efficient computation and storage, with an iterative algorithm ensuring convergence.

    Main Results:

    • MRKH demonstrates superior performance compared to state-of-the-art methods on five benchmark datasets, including three large-scale ones.
    • The method achieves high accuracy in large-scale image set classification and retrieval tasks.
    • Experimental results confirm the efficiency and scalability of MRKH, with linear computational complexity.

    Conclusions:

    • MRKH offers an effective and efficient solution for large-scale image set representation, classification, and retrieval.
    • The integration of Riemannian manifolds and hashing provides a powerful framework for complex data analysis.
    • The proposed method significantly advances the capabilities of image set analysis in real-world, large-scale scenarios.