Deep learning for logo recognition
In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried...
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Published in | Neurocomputing (Amsterdam) Vol. 245; pp. 23 - 30 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
05.07.2017
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Subjects | |
Online Access | Get full text |
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Summary: | In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synthetic versus real data augmentation, and image pre-processing. Moreover, we systematically investigate the benefits of different training choices such as class-balancing, sample-weighting and explicit modeling the background class (i.e. no-logo regions). Experimental results confirm the feasibility of the proposed method, that outperforms the methods in the state of the art. |
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ISSN: | 0925-2312 1872-8286 |
DOI: | 10.1016/j.neucom.2017.03.051 |