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Abstract
Automatic logo recognition is gaining importance due to the increasing number of its applications. Unlike other object recognition tasks, logo recognition is more challenging because of the limited amount of the available original data. In this paper, the transfer leaning technique was applied to a Deep Convolutional Neural Network model to guarantee logo recognition using a small computational overhead. The proposed method was based on the Densely Connected Convolutional Networks (DenseNet). The experimental results show that for the FlickrLogos-32 logo recognition dataset, our proposed method performs comparably with state-of-the-art methods while using fewer parameters.
Highlights
Logos are symbols that are generally used by firms to identify themselves and their products
The transfer learning technique was applied to a Deep Convolutional Neural Network (DCNN) model to ensure logo recognition without using huge computation resources and the results of the accuracy comparison with the state-of-the-art methods show that the proposed method performs
The proposed recognition pipeline includes a logo region proposal module followed by a Convolutional Neural Networks (CNNs) module that is trained for logo identification
Summary
Logos are symbols that are generally used by firms to identify themselves and their products. Convolutional Neural Networks (CNNs) with deep structure and many hidden layers is a very popular model that is commonly used for solving object recognition problems [18, 19]. Many techniques derived from CNNs have been used for solving the problem of logo recognition. Authors in [4, 5] used pre-trained CNNs for logo recognition. Such techniques have high computational overhead associated with them. The transfer learning technique was applied to a Deep Convolutional Neural Network (DCNN) model to ensure logo recognition without using huge computation resources and the results of the accuracy comparison with the state-of-the-art methods show that the proposed method performs
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