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Related Experiment Video

Updated: May 10, 2026

Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization
06:50

Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization

Published on: March 3, 2023

Joint optimization toward effective and efficient image search.

Shikui Wei, Dong Xu, Xuelong Li

    IEEE Transactions on Cybernetics
    |June 13, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances large-scale image search by optimizing the bag-of-words (BoW) model with embedding methods. Joint optimization improves both search effectiveness and efficiency, outperforming existing techniques.

    Related Experiment Videos

    Last Updated: May 10, 2026

    Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization
    06:50

    Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization

    Published on: March 3, 2023

    Area of Science:

    • Computer Science
    • Information Retrieval

    Background:

    • The bag-of-words (BoW) model is a foundational technique for large-scale image search and indexing.
    • Embedding methods have shown potential to improve BoW model performance, but their underlying mechanisms require further investigation.

    Purpose of the Study:

    • To systematically analyze factors influencing BoW model performance in image search.
    • To investigate how embedding methods can enhance BoW model effectiveness and efficiency.
    • To propose a jointly optimized image search scheme.

    Main Methods:

    • Systematic investigation of image search performance variations related to BoW model factors.
    • Application and study of embedding methods to improve BoW performance.
    • Development of an image search scheme by jointly optimizing BoW and embedding methods based on experimental observations.

    Main Results:

    • Identified key factors affecting BoW model performance in image search.
    • Demonstrated the benefits of employing embedding methods for improved search effectiveness.
    • Validated observations through experiments on descriptor matching and real-world image search scenarios.

    Conclusions:

    • Joint optimization of the BoW model and embedding methods yields significant improvements in image search.
    • The proposed scheme offers superior effectiveness and efficiency compared to state-of-the-art methods.
    • Understanding the working mechanism of BoW and embedding is crucial for advanced image retrieval systems.