Image based Monument Recognition using Graph based Visual Saliency

Georgios Triantafyllidis, Gregory Kalliatakis


This article presents an image-based application aiming at simple image classification of well-known monumentses 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 he had his/her image taken. Experiments are performed to verify that the proposed approach improves the time needed for the classification without affecting the correctness of the results.


SIFT, SURF, Graph Based Visual Saliency, Image classification

Full Text:

PDF (9216Kb)
Copyright (c)