Content-Based Image Retrieval Using Fourier Descriptors on a Logo Database
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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International Journal of Computer Vision
Trademark matching and retrieval in sports video databases
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LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Logo retrieval with a contrario visual query expansion
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Logo Matching for Document Image Retrieval
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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Proceedings of the international conference on Multimedia
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Point-context descriptor based region search for logo recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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Proceedings of the 20th ACM international conference on Multimedia
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The Visual Computer: International Journal of Computer Graphics
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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.