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Assessing Image Retrieval Quality at the First Glance.

Shaoyan Sun, Wengang Zhou, Qi Tian

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 15, 2018
    PubMed
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    This study introduces a new method to evaluate image retrieval quality using convolutional neural networks. The approach assesses top-ranked images for relevance, improving search result assessment without user feedback.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Image retrieval systems aim to provide relevant results, with top-ranked images being crucial for user satisfaction.
    • Evaluating retrieval quality in real-time without user feedback remains a significant challenge.
    • Existing methods struggle to assess the immediate value of top-ranked search results.

    Purpose of the Study:

    • To develop an efficient and accurate method for evaluating image retrieval quality based on top-ranked results.
    • To provide a system that can predict retrieval quality on-the-fly.
    • To align the evaluation metric with the Discounted Cumulative Gain (DCG) for better relevance assessment.

    Main Methods:

    • A convolutional neural network (CNN) regression model is employed for retrieval quality evaluation.
    • Correlation-based feature matrices incorporating contextual information from the retrieval list are computed.
    • Multiple visual features are integrated for robust and comprehensive representations.

    Main Results:

    • The proposed method demonstrates high prediction accuracy and consistency with ground truth.
    • The framework effectively evaluates retrieval quality using top-ranked images.
    • The system's practicability is shown in applications like rank list selection and database analysis.

    Conclusions:

    • The developed CNN-based approach offers a robust solution for on-the-fly image retrieval quality assessment.
    • This method enhances the evaluation of top-ranked results, aligning with DCG principles.
    • The technique has practical implications for improving image search engine performance and data management.