Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947443 | Neurocomputing | 2017 | 13 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Simone Bianco, Marco Buzzelli, Davide Mazzini, Raimondo Schettini,