比较基于深度学习的Logo识别体系结构

Gardyan Priangga Akbar, Eric Edgari, Bently Edyson, Nunung Nurul Qomariyah, Ardimas Andi Purwita
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引用次数: 0

摘要

标志识别是图像识别的一个子集,由于其特殊的问题受到了许多研究者的关注。也就是说,标志识别具有广泛的类内和类间变异性。例如,区分一个公司的新版本和旧版本的标志属于一个特定的问题,这是针对标志识别问题量身定制的。在本文中,我们比较了三种基于深度学习的标识识别架构,即Bianco的架构、AlexNet和Xception。Bianco的体系结构被选为包含许多预处理管道的体系结构样本,其中包括一个徽标区域建议。因此,在本文中,我们想要研究Bianco的架构是否在去除徽标区域建议的情况下比其他架构表现得更好。我们将其与其他典型的深度卷积神经网络架构(如AlexNet和Xception)进行比较。实验在FlickrLogo-32plus、FlickrLogos 27、BrandLogo和LogoDet-3K上进行。此外,我们还使用Selenium WebDriver添加了包含数百个徽标的精选数据集。我们发现Bianco的架构与AlexNet相比并没有明显的表现更好,与Xception相比表现更差。在此基础上,我们得出结论:标识区域建议是标识识别中一个重要的预处理步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing Deep Learning-based Architectures for Logo Recognition
Logo recognition is a subset of image recognition and has attracted attentions of many researchers due to its specific problem. That is, logo recognition has a wide intra-class and inter-class variability. For example, distinguishing a new edition of a company’s logo and the old one falls into a specific problem that is tailored to logo recognition problem. In this paper, we compare three deep learning-based logo recognition architectures, namely Bianco’s architecture, AlexNet, and Xception. Bianco’s architecture is chosen as a sample of an architecture that includes many preprocessing pipelines including a logo region proposal. Therefore, in this paper we want to investigate whether Bianco’s architecture performs significantly better compared to the others if a logo region proposal is removed. We compare it with other typical deep convolutional neural network architectures such as AlexNet and Xception. Experiments are carried out on the FlickrLogo-32plus, FlickrLogos 27, BrandLogo, and LogoDet-3K. In addition, we also add the curated dataset with hundreds of logo by using Selenium WebDriver. We found out that Bianco’s architecture does not significantly perform better compared to AlexNet, and performs worse compared to Xception. There, we conclude that a logo region proposal is an important preprocessing step in logo recognition.
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