Deep Learning for Logo Recognition

Simone Bianco, Marco Buzzelli, Davide Mazzini, Raimondo Schettini

2017-01-10Logo Recognition Image Classification Region Proposal Data Augmentation Deep Learning General Classification

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.

Results

Task	Dataset	Metric	Value	Model
Image Classification	FlickrLogos-32	Accuracy	96	TC-VII (with outside data)
Image Classification	FlickrLogos-32	Accuracy	91.7	TC-VII (without outside data)

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