A Semi-local Method for Image Retrieval

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

Abstract

The visual content of an image is expressed by global or local features. Global features describe some properties of the image such as color, texture and shape. Local features were successfully used for object category recognition and classification to extract the local information from a set of interest points or regions. In this paper, we propose a semi-local method to extract the features based on the previous features extraction methods. Our technique is called the “Spatial Pyramid Matching: SPM”. It works by partitioning the image into increasingly fine sub-regions (or blocs) and computing histograms of global features found inside each bloc. The results obtained by the proposed method are illustrated through some experiments on Wang and Holidays Dataset. The obtained Results show the simplicity and efficiency of our proposal.

Original languageEnglish
Title of host publicationIntelligent Systems Design and Applications - 18th International Conference on Intelligent Systems Design and Applications ISDA 2018
EditorsPatricia Melin, Ajith Abraham, Niketa Gandhi, Aswani Kumar Cherukuri
PublisherSpringer Verlag
Pages165-172
Number of pages8
ISBN (Print)9783030166595
DOIs
StatePublished - 2020
EventJoint Conferences on 18th International Conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018 - Vellore, India
Duration: 6 Dec 20188 Dec 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume941

Conference

ConferenceJoint Conferences on 18th International Conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018
Country/TerritoryIndia
CityVellore
Period6/12/188/12/18

Keywords

  • Features
  • Global descriptors
  • Image retrieval
  • Local descriptors
  • Semi-local
  • Spatial Pyramid Matching
  • Visual content

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