FARA: fast and accurate RFDoc descriptor approximation

Artem Sher; Anton Trusov; Mikhail Maksimenko; Nikita Arlazarov; Elena Limonova

doi:10.1117/12.3023417

3 April 2024 FARA: fast and accurate RFDoc descriptor approximation

Artem Sher, Anton Trusov, Mikhail Maksimenko, Nikita Arlazarov, Elena Limonova

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 130720F (2024) https://doi.org/10.1117/12.3023417
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

Abstract

We present FARA, a novel approach for fast approximation of RFD-like descriptors in the context of document retrieval systems. RFD-like descriptors are widely used for document representation, but their computation is expensive, especially for large document collections. Our method is a CPU-friendly gradient maps computation approximation with sequential memory access and integer-only calculations. There are three types of operations that we use: addition, subtraction, and absolute values. It allows us to effectively use SIMD extensions, resulting in an additional increase in the running speed. Experimental results demonstrate that FARA achieves the same accuracy as RFDoc descriptors and significantly reduces the computational overhead. The proposed approach achieves a twofold speed improvement of gradient maps computation and 25% acceleration of overall descriptor computing time compared to the most efficient RFDoc implementation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Artem Sher, Anton Trusov, Mikhail Maksimenko, Nikita Arlazarov, and Elena Limonova "FARA: fast and accurate RFDoc descriptor approximation", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 130720F (3 April 2024); https://doi.org/10.1117/12.3023417

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