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Cleaning, Sterilization, and Disinfection

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Related Experiment Video

Updated: Jun 21, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Content based image retrieval using unclean positive examples.

Jun Zhang, Lei Ye

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 10, 2009
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for image retrieval that effectively handles noisy positive examples. The approach combines multiple feature distances and uses a two-step strategy for data cleaning and noise-tolerant classification, improving retrieval accuracy.

    Related Experiment Videos

    Last Updated: Jun 21, 2026

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    Area of Science:

    • Computer Science
    • Information Retrieval

    Background:

    • Conventional content-based image retrieval (CBIR) faces challenges with user interaction, labeling effort, and noisy data.
    • Users often prefer single-interaction searches, making extensive feedback impractical.
    • Difficulty in labeling diverse negative examples and potential for noisy positive examples hinder CBIR performance.

    Discussion:

    • The proposed scheme addresses image retrieval with unclean positive examples.
    • It combines multiple feature distances for similarity calculation using classification technology.
    • A novel two-step strategy integrates data cleaning and noise-tolerant classification.

    Key Insights:

    • The method effectively handles noisy positive examples in image retrieval.
    • Combining multiple feature distances enhances similarity assessment.
    • The two-step strategy improves robustness against imperfect user input.

    Outlook:

    • This approach offers a more practical solution for real-world CBIR applications.
    • Further research could explore adaptive feature weighting or different classification models.
    • Validation on diverse datasets confirms the scheme's effectiveness.