Trademarks are used by companies to help customers identify products or services using images or logos in addition to slogans, words, names, sounds, smells, color, and motions. Trademark logos are widely distributed through advertising and published through online media websites and social networks such as Facebook, Pinterest, and Flicker. The intellectual property (IP) rights of the trademark owners have strong legal protection when registered with international intellectual property platforms such as the US Patent and Trademark Office and the World Intellectual Property Office. Using a registered trademark without prior consent of the owner may result in intellectual property infringement with severe legal consequences under civil or criminal law. Companies invest large capital resources in protecting their trademark from being copied or misused in ways that confuse the customers or steal market share. This research focuses on trademark (TM) logo image retrieval systems used in the cyber marketplaces to identify similar TM logo images online automatically and intelligently. The methodology developed for TM logo similarity measurement is based on content-based image retrieval. Content retrieval reduces the gap between high-level semantic interpretation of human vision and the low-level features processed by the machine. The proposed transfer learning methodology uses embedded learning with triplet loss to fine-tune a pre-trained convolutional neural network model. The Logo-2K+ large-scale logo dataset is re-organized and divided into the top 70% as the training set and the remaining 30% as the testing set. The results show that the novel transfer learning approach is developed and demonstrated in this research for the intelligent automatic detection of similar TM logo images with high accuracy. The verification experiments (trained with 7625 logos and tested with 3221 logos) demonstrates that the Recall@10 of the test set can reach 95% using the advanced convolutional neural network model (VGG19) adjusted with the novel transfer learning methodology.

公司使用商标来帮助客户使用图像或徽标以及口号，单词，名称，声音，气味，颜色和动作来标识产品或服务。商标徽标通过广告广泛分发，并通过在线媒体网站和社交网络（如Facebook，Pinterest和Flicker）发布。在国际知识产权平台（例如美国专利商标局和世界知识产权局）注册时，商标所有者的知识产权（IP）具有强大的法律保护。未经所有者事先同意而使用注册商标可能会导致知识产权侵权，并根据民法或刑法产生严重的法律后果。公司投入大量的资本资源来保护其商标，以免引起客户混淆或抢占市场份额的方式被复制或滥用。这项研究专注于网络市场中使用的商标（TM）徽标图像检索系统，以自动，智能地在线识别相似的TM徽标图像。为TM徽标相似度测量而开发的方法基于基于内容的图像检索。内容检索减少了人类视觉的高级语义解释与机器处理的低级特征之间的差距。提出的转移学习方法使用具有三重态损失的嵌入式学习来微调预训练的卷积神经网络模型。重新组织了Logo-2K +大型徽标数据集，并将其划分为前70％作为训练集，其余30％作为测试集。结果表明，这种新颖的转移学习方法在本研究中得到了开发和证明，可以智能地自动高精度地检测相似的TM徽标图像。验证实验（经过培训的7625个徽标和3221个徽标进行了测试）表明，使用通过新颖的转移学习方法调整的高级卷积神经网络模型（VGG19），测试集的Recall @ 10可以达到95％。