Recognition of Graffiti Tags
Paul Schwarz
Abstract
Graffiti tagging, the inscription of an individual graffiti writer's
unique mark in public space, is considered to be an antisocial
behaviour and is a criminal offense. Due to the costs involved in the
removal of graffiti tags it would be desirable to deter tagging. A
searchable database of graffiti images could be an integral part of
the policing system aimed at deterring graffiti vandalism. In order to
index the database, an algorithm must be used that is capable of
describing and comparing images. There are various approaches to
object recognition and shape matching. The two methods that have been
examined in this thesis are the Scale-Invariant Feature Transform
(SIFT) and the Shape Context. These techniques were chosen based on
their suitability to deal with the types of inconsistencies that are
common in graffiti tag recognition, compared with the variances that
occur in generic object recognition. Shape Context was found to be
better suited as it was more robust than SIFT with regard to small
shape distortions that are natural in graffiti writing. An empirical
measure for the correctness of a searchable graffiti database was
devised. Using this measure, it was found that retrieval results using
Shape Context were considerably more accurate than retrieval results
using SIFT. This thesis introduces the problem of recognising graffiti
tags into the areas of object recognition and shape matching. Results
are presented based on a collection of photographed graffiti tags.
Thesis
Zip archive of graffiti images used
in this work.