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Machine Vision and Applications

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01.02.2016 | Original Paper

Logo localization and recognition in natural images using homographic class graphs

verfasst von: Raluca Boia, Corneliu Florea, Laura Florea, Radu Dogaru

Erschienen in: Machine Vision and Applications | Ausgabe 2/2016

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Abstract

We propose a method for localization and classification of brand logos in natural images. The system has to overcome multiple challenges such as perspective deformations, warping, variations of the shape and colors, occlusions, background variations. To deal with perspective variation, we rely on homography matching between the SIFT keypoints of logo instances of the same class. To address the changes in color, we construct a weighted graph of logo interconnections that is further analyzed to extract potentially multiple instances of the class. The main instance is built by grouping the keypoints of the graph connected logos onto the central image. The secondary instance is needed for color inverted logos and is obtained by inverting the orientation of the main instance. The constructed logo recognition system is tested on two databases (FlickrLogos-32 and BelgaLogos), outperforming state of the art with more than 10 % accuracy.

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Confusion matrix, and other supplementary results may be retrieved from the project page http://imag.pub.ro/common/staff/rboia/logoRecognition/.

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Titel: Logo localization and recognition in natural images using homographic class graphs
verfasst von: Raluca Boia
Corneliu Florea
Laura Florea
Radu Dogaru
Publikationsdatum: 01.02.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 2/2016
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-015-0741-7

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