# Trademark Image Similarity Detection Using Convolutional Neural Network
## 2 Related Work  
The increasing number of images being registered as trademarks emphasize the need to automate the tasks of dealing with them, such as image detection, classification, and retrieving similar trademarks accurately. In particular, there is an urgent need to prevent trademark infringements by harnessing machine learning approaches to efficiently detect similarities. This section gives a short overview of previous studies of the trademark image detection and retrieval approaches. One of the earliest studies in trademark detection was [PERSON] and [PERSON]'s [8] study in 1998. They proposed an approach for trademark retrieval based on relevant feedback. The user's perspective directly affects the tuning of the similarity measure used by [PERSON] and [PERSON]'s system. The feature vector of the input trademark was compared with those available in the database and assigned a weight for each to retrieve the higher-ranking images. Then, the user's feedback was gathered as relevant or irrelevant to the input image to consider the image features for a second iteration. The results showed that user feedback could improve the measure of similarity and enhance the retrieval process. In the same manner, [PERSON] et al. [9] proposed a framework for trademark recognition and retrieval that integrated CNN with relevance feedback (RF) mechanism. The featuresare optimized using particle swarm optimization and self-organizing map methods as a preprocessing phase to reduce the search space. The framework was tested using the FlickrLogos-32 PLUS dataset. Comparing the proposed framework to methods based on deep learning models alone emphasized the positive impact of combining CNN with an RF mechanism. CNNs have been efficiently used with most image classification, detection, and recognition tasks. [PERSON] et al. [10] proposed a method based on a pretrained CNN called VGG19 for trademark image retrieval. The VGG19 network was trained using two different databases. The first, VGG19v, concentrated on visual similarities, and the second, VGG19c, focused on conceptual similarities. The first database contained logos collected from the web and then categorized these into 151 classes based on similarities defined by experts. Further, the second database depended on manually labeled USPTO logos. Both networks complemented each other in the classification task. Combining VGG19v and VGG19c improves the accuracy of image detection tasks. Additionally, [PERSON] et al. [11] proposed a trademark retrieval system based on CNN and constraints theory. The Siamese neural network (SNN) model was constructed using VGG-F with a pretrained CNN. The Middle East Technical University (METU) dataset was used to evaluate the proposed method. These experiments showed the effectiveness of the SNN and CNN-Triplet features. In [12], the authors proposed a method to detect logos in real-world images. The framework can be divided into two parts: conditioning and segmentation. In conditioning, CNNs are used to extract the features from the query image. The network consists of 13 convolution layers; each of which is activated using the rectified linear unit function. A modified version of the U-Net CNN architecture was used for the purpose of image segmentation, taking into consideration the different scale representations of logos at the encoder end of the U-Net. The proposed method achieved a mean average precision (mAP) score of 89.2, which is the highest score compared to some of the recent logo detection methods. In trademark images, the figurative component is considered the most critical region in the image. In [13], the authors' proposed methods were based on hard- and soft attention mechanisms. U-Net architecture, a fully convolutional network, was trained using focal loss and was used to separate the images into text and nontext pixels for hard attention. Convolutional activation maps were used to assign a weight to the images' symbolic and text components for soft attention.