Scopus: 5 cites, Google Scholar: cites
Image based Monument Recognition using Graph based Visual Saliency
Triantafyllidis, Georgios (Aalborg University Copenhagen. Medialogy Section)
Kalliatakis, Gregory (Technological Educational Institute of Crete. Applied Informatics and Multimedia Department)

Data: 2013
Resum: 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: SIFT ; SURF ; Graph Based Visual Saliency ; Image classification
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 12, Núm. 2 (2013) , p. 88-97, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.524

9 p, 9.8 MB

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