Image based Monument Recognition using Graph based Visual Saliency

Grigorios Kalliatakis, Georgios Triantafyllidis

Research output: Contribution to journalJournal articleResearchpeer-review

5 Citations (Scopus)
858 Downloads (Pure)

Abstract

This article presents an image-based application aiming at simple image classification of well-known monuments in the area of Heraklion, Crete, Greece. This classification takes place by utilizing Graph Based Visual Saliency (GBVS) and employing Scale Invariant Feature Transform (SIFT) or Speeded Up Robust
Features (SURF). For this purpose, images taken at various places of interest are being compared to an existing database containing images of these places at different angles and zoom. The time required for the matching progress in such application is an important element. To this goal, the images have been previously processed according to the Graph Based Visual Saliency model in order to keep either SIFT or SURF features corresponding to the actual monuments while the background “noise” is minimized. The application is then able to classify these images, helping the user to better understand what he/she sees and in which area the image has been taken. Experiments are performed to verify that the proposed approach improves the time needed for the classification without affecting the correctness of the results.
Original languageEnglish
JournalElectronic Letters on Computer Vision and Image Analysis
Volume12
Issue number2
Pages (from-to)88-97
Number of pages10
ISSN1577-5097
Publication statusPublished - 2013

Keywords

  • SIFT
  • SURF
  • Graph Based Visual Saliency
  • Image classification

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