Efficient pose machine based on parameter-sensitive hashing

Shangchi Lin, Bowen Liu, Yang Wen, Anum Masood, Bin Sheng, Ping Li, Xin Liu, Haoyang Yu, Weiyao Lin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In this paper, we propose an efficient pose machine using Parameter-Sensitive Hashing(PSH) techniques. Based on the original pose machine, which is a sequential prediction framework, we employ the Convolutional Neural Network(CNN) to extract features. To handle the high dimensional feature vectors and conduct similarity search efficiently, we use the Parameter-Sensitive Hashing Function(PSHF) to map the feature vectors into binary values. The property of the PSHF ensures that the collisions happen when two vectors are near to each other and the search can be completed in a fractional power time. We apply our approach to the popular datasets including LSP and FLIC and make a comparison with previous methods based on a criterion of strict Percentage of Correct Parts(PCP). Experimental results reflect that our approach outperforms previous methods in accuracy.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Progress in Informatics and Computing, PIC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages450-454
Number of pages5
ISBN (Electronic)9781538619773
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event5th International Conference on Progress in Informatics and Computing, PIC 2017 - Nanjing, China
Duration: 15 Dec 201717 Dec 2017

Publication series

NameProceedings of 2017 International Conference on Progress in Informatics and Computing, PIC 2017

Conference

Conference5th International Conference on Progress in Informatics and Computing, PIC 2017
Country/TerritoryChina
CityNanjing
Period15/12/1717/12/17

Keywords

  • Convolutional neural network
  • Example-based method
  • Human pose estimation
  • Parameter estimation
  • Parameter-sensitive hashing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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