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Histograms of locally aggregated oriented gradients

  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Motivated by the Vector of Locally Aggregated Descriptors (VLAD), we propose a new Histograms of Locally Aggregated Oriented Gradients descriptor (called HLAOG). In the Histograms of Oriented Gradients descriptor (HOG), the zero-order information of the gradients is captured. By contrast, in the HLAOG descriptor we accumulate the differences between gradient orientations and their nearest bin centers, which characterizes the distribution of the gradient orientations in regard to the bin centers. The HLAOG descriptor is demonstrated to be complementary to HOG in the experiments. Then, for setting the weights of the votes on different bins in a better way, we choose Gaussian function as the weighting method and present another new Gaussian Weighted Histograms of Oriented Gradients descriptor (called GWHOG) based on HOG. Evaluations on two public object recognition datasets (Caltech-101 and VOC2007) show that the combination of HOG and HLAOG outperforms HOG and the combination of HLAOG and GWHOG gets the best result.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages1270-1274
Number of pages5
ISBN (Electronic)9781479983391
DOIs
StatePublished - 9 Dec 2015
Externally publishedYes
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • HOG
  • Object recognition
  • VLAD
  • image descriptor

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