Scalable triangulation-based logo recognition

Kalantidis, Y; Pueyo, LG; Trevisiol, M; Van Zwol, R; Avrithis, Y

dc.contributor.author	Kalantidis, Y	en
dc.contributor.author	Pueyo, LG	en
dc.contributor.author	Trevisiol, M	en
dc.contributor.author	Van Zwol, R	en
dc.contributor.author	Avrithis, Y	en
dc.date.accessioned	2014-03-01T02:53:27Z
dc.date.available	2014-03-01T02:53:27Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36330
dc.subject.other	Bag of words	en
dc.subject.other	Class models	en
dc.subject.other	Data sets	en
dc.subject.other	Delaunay triangulation	en
dc.subject.other	Indexing process	en
dc.subject.other	Inverted index structures	en
dc.subject.other	Local geometry	en
dc.subject.other	Logo recognition	en
dc.subject.other	Multiscales	en
dc.subject.other	Query images	en
dc.subject.other	Search problem	en
dc.subject.other	Triangulation-based	en
dc.subject.other	Visual appearance	en
dc.subject.other	Data processing	en
dc.subject.other	Visualization	en
dc.subject.other	Triangulation	en
dc.title	Scalable triangulation-based logo recognition	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/1991996.1992016	en
heal.identifier.secondary	http://dx.doi.org/10.1145/1991996.1992016	en
heal.publicationDate	2011	en
heal.abstract	We propose a scalable logo recognition approach that extends the common bag-of-words model and incorporates local geometry in the indexing process. Given a query image and a large logo database, the goal is to recognize the logo contained in the query, if any. We locally group features in triples using multi-scale Delaunay triangulation and represent triangles by signatures capturing both visual appearance and local geometry. Each class is represented by the union of such signatures over all instances in the class. We see large scale recognition as a sub-linear search problem where signatures of the query image are looked up in an inverted index structure of the class models. We evaluate our approach on a large-scale logo recognition dataset with more than four thousand classes. © 2011 ACM.	en
heal.journalName	Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR'11	en
dc.identifier.doi	10.1145/1991996.1992016	en