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Unsupervised Image Region of Interest Detection using Discrete Cosine Transform

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

Abstract

Spatial domain analysis of images has been recognised as an important task in image retrieval and computer vision, however, its application in the determination of image Region of Interests often require the inclusion of supervised training and similarity computations, both of which can be difficult to implement during the modelling of the images in a large number of images. however, unsupervised image Region of Interest determination via Frequency domain analysis can be implemented without training or similarity computation therefore it is more suitable for the modelling of images in a large set. This paper demonstrates image frequency domain analysis via Discrete Cosine Transform and presents its application in the unsupervised determination of Region of Interests for the elimination of the spatial incoherency often associated with the Bag-of-Visual Word Modelling.

Original languageEnglish
Title of host publication2021 4th International Conference on Information Communication and Signal Processing, ICICSP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages348-351
Number of pages4
ISBN (Electronic)9781665407571
DOIs
Publication statusPublished - 2021
Event4th International Conference on Information Communication and Signal Processing, ICICSP 2021 - Shanghai, China
Duration: 24 Sept 202126 Sept 2021

Publication series

Name2021 4th International Conference on Information Communication and Signal Processing, ICICSP 2021

Conference

Conference4th International Conference on Information Communication and Signal Processing, ICICSP 2021
Country/TerritoryChina
CityShanghai
Period24/09/2126/09/21

Keywords

  • Bag-of-Visual Words
  • Discrete Cosine Transform
  • Frequency domain analysis
  • Region of Interest
  • Spatial domain analysis
  • Unsupervised image classification

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