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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Traditional hashing models for image retrieval assume closed-set environments, limiting real-world applicability.
    • Online incremental learning and adapting to expanding concept spaces are critical for practical image retrieval systems.

    Purpose of the Study:

    • To develop a hashing paradigm supporting rehearsal-free online incremental coding for data streams.
    • To enable adaptive code lengths and bit extension without knowledge forgetting in open environments.
    • To address the limitations of closed-set assumptions in large-scale image retrieval.

    Main Methods:

    • Propose Bit-extendable IncremenTal haShing (BITS), a method with dynamically adjusted bit lengths based on encountered categories.
    • Implement a three-phase framework for efficient incremental hashing, including online continual coding and bit extension.
    • Utilize a blurry incremental setup to simulate realistic open-environment scenarios, evaluating against data-incremental and class-incremental settings.

    Main Results:

    • BITS achieves performance comparable to offline hashing methods while significantly reducing computational costs.
    • Demonstrates superiority in dynamic scenarios across six benchmark datasets.
    • Effectively handles online incremental coding and adaptive code length adjustments without knowledge forgetting.

    Conclusions:

    • BITS offers an effective solution for image retrieval in open environments with dynamic concept spaces.
    • The proposed method balances performance and computational efficiency for practical applications.
    • BITS advances the field of incremental hashing by supporting adaptive and extensible hash codes.