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arXiv · 1701.02620

Deep Learning for Logo Recognition

Simone Bianco·Marco Buzzelli·Davide Mazzini·Raimondo Schettini
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Abstract

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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Simone Bianco, Marco Buzzelli, Davide Mazzini, Raimondo Schettini. 2017-01-10. Deep Learning for Logo Recognition. https://doi.org/10.1016/j.neucom.2017.03.051

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