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The Inverted Multi-Index.

Artem Babenko, Victor Lempitsky

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 11, 2015
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    Summary
    This summary is machine-generated.

    A novel data structure, the inverted multi-index, enhances similarity search in large, high-dimensional datasets. It offers improved recall and speed for approximate nearest neighbor search with minimal memory overhead.

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    Area of Science:

    • Computer Science
    • Data Structures
    • Information Retrieval

    Background:

    • Efficient similarity search is crucial for large, high-dimensional datasets.
    • Existing methods like inverted indices face scalability challenges.

    Purpose of the Study:

    • Introduce a new data structure, the inverted multi-index, for efficient similarity search.
    • Improve recall and speed of approximate nearest neighbor search.

    Main Methods:

    • Generalize inverted indices using product quantization.
    • Subdivide search space more densely than standard inverted indices.
    • Experiment with large SIFT and GIST vector datasets.

    Main Results:

    • Inverted multi-indices achieve denser search space subdivision.
    • Shorter candidate lists with higher recall are returned.
    • Significant speed improvements for approximate nearest neighbor search on 1 billion SIFT vectors were observed.

    Conclusions:

    • Inverted multi-indices offer superior performance for similarity search in massive datasets.
    • The structure provides a memory-efficient and highly scalable solution.
    • It outperforms previous systems in speed and recall for approximate nearest neighbor search.