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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Near duplicate image detection is crucial for managing large image datasets.
    • Existing methods often struggle with efficiency and scalability.
    • Hierarchical hash code learning and locality-sensitive hashing (LSH) offer potential solutions.

    Purpose of the Study:

    • To develop an efficient and effective method for near duplicate image detection.
    • To leverage deep learning for feature extraction and hash code generation.
    • To improve the performance of LSH indexing through load balancing.

    Main Methods:

    • A deep constrained siamese hash coding neural network was developed for feature extraction.
    • A load-balanced locality-sensitive hashing (LSH) method was proposed for index construction.
    • The integrated approach was evaluated on benchmark datasets.

    Main Results:

    • The proposed deep siamese hash coding network effectively extracts discriminative features for image matching.
    • Load-balanced LSH significantly reduces query time by distributing data evenly.
    • The combined method demonstrates superior performance in near duplicate image detection.

    Conclusions:

    • The novel method provides an efficient and feasible solution for near duplicate image detection.
    • Deep feature learning combined with load-balanced LSH enhances detection accuracy and speed.
    • The approach is validated by extensive experiments on benchmark datasets.