Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets

Dmitry Pozdnyakov

doi:doi:10.11648/j.ajcst.20210402.12

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Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets

Dmitry Pozdnyakov

Published in American Journal of Computer Science and Technology (Volume 4, Issue 2)

Received: 25 June 2021 Accepted: 16 July 2021 Published: 24 July 2021

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Abstract

The algorithm of effective geometric hashing of the facial feature hyperspace for the accelerated search of the most similar face descriptors by their cosine similarity is described in the present study. The algorithm includes 6 required stages of processing descriptors extracted by a neural network from face images. The first stage is filtration of the descriptor database by selecting the most representative descriptor for each person from the set of descriptors corresponding to his/her different face images. The second stage is evaluation of a number of statistical values for all the components of the selected descriptors. The third stage is intermediate hashing through quantization of every descriptor component value so that almost the same quantity of descriptors corresponds to any quantum number. The fourth stage is statistical processing of the descriptor database to determine the most discriminative descriptor key components and their hierarchy. The fifth stage is calculation of the descriptor hash code for every most representative descriptor from the considered database. The sixth final stage is a special cataloging of data in the form of a multi-tiered directory ordered by the hash codes. The search acceleration is achieved through sparse processing of the whole directory when the hash code obtained for the requested person descriptor acts as a very selective search filter. The developed algorithm always provides the same absolute accuracy as the brute-force search. Through the example of the LFW dataset consideration, the average search acceleration by about 100 times is achieved under conditions that the descriptors have been extracted by a neural network trained on the WiderFace dataset with application of the additive angular margin loss function.

Published in	American Journal of Computer Science and Technology (Volume 4, Issue 2)
DOI	10.11648/j.ajcst.20210402.12
Page(s)	38-45
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Geometric Hashing, Brute Force, Search, Feature Hyperspace, Descriptor, Cosine Similarity

References

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Dmitry Pozdnyakov. (2021). Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets. American Journal of Computer Science and Technology, 4(2), 38-45. https://doi.org/10.11648/j.ajcst.20210402.12

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ACS Style

Dmitry Pozdnyakov. Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets. Am. J. Comput. Sci. Technol. 2021, 4(2), 38-45. doi: 10.11648/j.ajcst.20210402.12

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AMA Style

Dmitry Pozdnyakov. Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets. Am J Comput Sci Technol. 2021;4(2):38-45. doi: 10.11648/j.ajcst.20210402.12

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@article{10.11648/j.ajcst.20210402.12,
  author = {Dmitry Pozdnyakov},
  title = {Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets},
  journal = {American Journal of Computer Science and Technology},
  volume = {4},
  number = {2},
  pages = {38-45},
  doi = {10.11648/j.ajcst.20210402.12},
  url = {https://doi.org/10.11648/j.ajcst.20210402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20210402.12},
  abstract = {The algorithm of effective geometric hashing of the facial feature hyperspace for the accelerated search of the most similar face descriptors by their cosine similarity is described in the present study. The algorithm includes 6 required stages of processing descriptors extracted by a neural network from face images. The first stage is filtration of the descriptor database by selecting the most representative descriptor for each person from the set of descriptors corresponding to his/her different face images. The second stage is evaluation of a number of statistical values for all the components of the selected descriptors. The third stage is intermediate hashing through quantization of every descriptor component value so that almost the same quantity of descriptors corresponds to any quantum number. The fourth stage is statistical processing of the descriptor database to determine the most discriminative descriptor key components and their hierarchy. The fifth stage is calculation of the descriptor hash code for every most representative descriptor from the considered database. The sixth final stage is a special cataloging of data in the form of a multi-tiered directory ordered by the hash codes. The search acceleration is achieved through sparse processing of the whole directory when the hash code obtained for the requested person descriptor acts as a very selective search filter. The developed algorithm always provides the same absolute accuracy as the brute-force search. Through the example of the LFW dataset consideration, the average search acceleration by about 100 times is achieved under conditions that the descriptors have been extracted by a neural network trained on the WiderFace dataset with application of the additive angular margin loss function.},
 year = {2021}
}

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TY  - JOUR
T1  - Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets
AU  - Dmitry Pozdnyakov
Y1  - 2021/07/24
PY  - 2021
N1  - https://doi.org/10.11648/j.ajcst.20210402.12
DO  - 10.11648/j.ajcst.20210402.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 38
EP  - 45
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20210402.12
AB  - The algorithm of effective geometric hashing of the facial feature hyperspace for the accelerated search of the most similar face descriptors by their cosine similarity is described in the present study. The algorithm includes 6 required stages of processing descriptors extracted by a neural network from face images. The first stage is filtration of the descriptor database by selecting the most representative descriptor for each person from the set of descriptors corresponding to his/her different face images. The second stage is evaluation of a number of statistical values for all the components of the selected descriptors. The third stage is intermediate hashing through quantization of every descriptor component value so that almost the same quantity of descriptors corresponds to any quantum number. The fourth stage is statistical processing of the descriptor database to determine the most discriminative descriptor key components and their hierarchy. The fifth stage is calculation of the descriptor hash code for every most representative descriptor from the considered database. The sixth final stage is a special cataloging of data in the form of a multi-tiered directory ordered by the hash codes. The search acceleration is achieved through sparse processing of the whole directory when the hash code obtained for the requested person descriptor acts as a very selective search filter. The developed algorithm always provides the same absolute accuracy as the brute-force search. Through the example of the LFW dataset consideration, the average search acceleration by about 100 times is achieved under conditions that the descriptors have been extracted by a neural network trained on the WiderFace dataset with application of the additive angular margin loss function.
VL  - 4
IS  - 2
ER  -

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Author Information

Dmitry Pozdnyakov

Deep Learning Department, Closed Joint Stock Company "Oxagile", Minsk, Belarus

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APA Style

Dmitry Pozdnyakov. (2021). Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets. American Journal of Computer Science and Technology, 4(2), 38-45. https://doi.org/10.11648/j.ajcst.20210402.12

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ACS Style

Dmitry Pozdnyakov. Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets. Am. J. Comput. Sci. Technol. 2021, 4(2), 38-45. doi: 10.11648/j.ajcst.20210402.12

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AMA Style

Dmitry Pozdnyakov. Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets. Am J Comput Sci Technol. 2021;4(2):38-45. doi: 10.11648/j.ajcst.20210402.12

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@article{10.11648/j.ajcst.20210402.12,
  author = {Dmitry Pozdnyakov},
  title = {Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets},
  journal = {American Journal of Computer Science and Technology},
  volume = {4},
  number = {2},
  pages = {38-45},
  doi = {10.11648/j.ajcst.20210402.12},
  url = {https://doi.org/10.11648/j.ajcst.20210402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20210402.12},
  abstract = {The algorithm of effective geometric hashing of the facial feature hyperspace for the accelerated search of the most similar face descriptors by their cosine similarity is described in the present study. The algorithm includes 6 required stages of processing descriptors extracted by a neural network from face images. The first stage is filtration of the descriptor database by selecting the most representative descriptor for each person from the set of descriptors corresponding to his/her different face images. The second stage is evaluation of a number of statistical values for all the components of the selected descriptors. The third stage is intermediate hashing through quantization of every descriptor component value so that almost the same quantity of descriptors corresponds to any quantum number. The fourth stage is statistical processing of the descriptor database to determine the most discriminative descriptor key components and their hierarchy. The fifth stage is calculation of the descriptor hash code for every most representative descriptor from the considered database. The sixth final stage is a special cataloging of data in the form of a multi-tiered directory ordered by the hash codes. The search acceleration is achieved through sparse processing of the whole directory when the hash code obtained for the requested person descriptor acts as a very selective search filter. The developed algorithm always provides the same absolute accuracy as the brute-force search. Through the example of the LFW dataset consideration, the average search acceleration by about 100 times is achieved under conditions that the descriptors have been extracted by a neural network trained on the WiderFace dataset with application of the additive angular margin loss function.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Effective Geometric Hashing of the Feature Hyperspace for a Quick Accurate Search of the Most Similar Descriptors in Large Datasets
AU  - Dmitry Pozdnyakov
Y1  - 2021/07/24
PY  - 2021
N1  - https://doi.org/10.11648/j.ajcst.20210402.12
DO  - 10.11648/j.ajcst.20210402.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 38
EP  - 45
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20210402.12
AB  - The algorithm of effective geometric hashing of the facial feature hyperspace for the accelerated search of the most similar face descriptors by their cosine similarity is described in the present study. The algorithm includes 6 required stages of processing descriptors extracted by a neural network from face images. The first stage is filtration of the descriptor database by selecting the most representative descriptor for each person from the set of descriptors corresponding to his/her different face images. The second stage is evaluation of a number of statistical values for all the components of the selected descriptors. The third stage is intermediate hashing through quantization of every descriptor component value so that almost the same quantity of descriptors corresponds to any quantum number. The fourth stage is statistical processing of the descriptor database to determine the most discriminative descriptor key components and their hierarchy. The fifth stage is calculation of the descriptor hash code for every most representative descriptor from the considered database. The sixth final stage is a special cataloging of data in the form of a multi-tiered directory ordered by the hash codes. The search acceleration is achieved through sparse processing of the whole directory when the hash code obtained for the requested person descriptor acts as a very selective search filter. The developed algorithm always provides the same absolute accuracy as the brute-force search. Through the example of the LFW dataset consideration, the average search acceleration by about 100 times is achieved under conditions that the descriptors have been extracted by a neural network trained on the WiderFace dataset with application of the additive angular margin loss function.
VL  - 4
IS  - 2
ER  -

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