An Accelerated and Flexible SIFT Parallel-Computing Approach Based on the General Multi-Core Platform

Gang WANG, Mingliang ZHOU*, Bin FANG, Haichao HUANG, Zhenyu SHU, Xueshu CHEN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Visual retrieval has been a significant technology in the computer vision task. Visual feature descriptors are the key to the visual retrieval. The famous local feature descriptor is called the Scale Invariant Feature Transform (SIFT), which can keep invariant mapping for the scale, rotate and simulate images. To utilize effectively the SIFT feature descriptor for visual matching on different hardware platforms, this paper proposes an accelerated SIFT algorithm based on the SIFT feature computing principle of the general multi-core platform. First, our multi-core task allocation method introduces the WFM theory into task assignment for each core to improve the core computing resource utilization for high-efficient parallel computing. Then, to improve the efficiency of picture matching, we introduce global geometric constraints condition to optimal picture matching for the multi-core parallelization approach. Experimental results show that the proposed approach can save on average 87.31% on the Intel X86 platform, compared to the single-core time. Also, our approach can save on average 33.79% on the Raspberry Pi platform, compared to the single-core time.

Original languageEnglish
Article number2255010
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume36
Issue number6
Early online date28 Mar 2022
DOIs
Publication statusPublished - May 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 World Scientific Publishing Company.

Keywords

  • Hardware platforms
  • Multi-core parallelization approach
  • SIFT
  • Task assignment

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