# Trademark Image Similarity Detection Using Convolutional Neural Network
## 1 Introduction  
Object detection is an important field in artificial intelligence (AI). Today, there are many computer vision applications based on object detection in different contexts. Accordingly, a wide variety of detection models have been proposed in the literature [1, 2]. Similarity detection and measures is an emerging field of research in object detection. A trademark is a form of intellectual property (IP) consisting of a recognizable sign that identifies and distinguishes a product or service from others. It can be text, a phrase, a figurative symbol, a design, or any combination of these. The importance of logos and trademarks in building a brand is unquestionable, even for small businesses. A trademark mirrors the quality and professionalism of the product or service provided. Most customers rely on the trademark's reputation to make purchase decisions and compare the quality of two products [3, 4]. Therefore, the added value and additional profit that trademarks endow to their products exceed their real functional benefits [4]. In 2018, the number of recorded trademark registrations worldwide was approximately equal to 10.9 million, an increase of 19.2%, compared with 2017 [5]. Some processes should be conducted to ensure that the new mark does not cause confusion with prior registered trademarks and does not match any others in the case of anew trademark [6]. In visual terms, the meaning of \"matching\" includes a combination of different parameters, such as colors, shape, and texture. Nowadays, a number of regional and national IP offices around the world deal with the processes of trademark examination and registration [6; 7]. Examples include the European Union Intellectual Property Office and the United States Patent and Trademark Office (USPTO), which serve these functions. In Saudi Arabia, the Saudi Authority for Intellectual Property is the official government body responsible for protecting and supporting IP rights in the Kingdom [7]. Any new trademark should be examined to ensure no confusion or close similarity to any other prior registered marks. In a manual approach, the examination process for any trademark requires a long time as a result of the increasing annual number of registered trademarks. Additionally, the process could be susceptible to error because judging whether two trademarks are similar is subjective and varies from one examiner to another depending on their experience. The automation of the examination process using AI that facilitates the retrieval of similarity-based trademarks can provide significant help for examiners to achieve higher accuracy. The AI can provide a limited number of most similar trademarks that need to be examined rather than a large database of marks. Although there are already some systems intended to support examiners in the trademark examination process, these systems depend on manually annotated trademarks. Therefore, they are subjective, time-consuming, and error-prone. Consequently, this study develops a trademark similarity detection system using deep learning that can automatically and accurately detect and retrieve registered trademarks that are closest in similarity to an input mark. The contribution of this study is to provide a framework and comprehensive background for trademark similarity experiments as an examination tool in the trademark registration process. The proposed framework presents a system that reads an image (including the various image formats), extracts its features, and finds the similarity between a given image and each of those stored based on their distance. We investigated the use of convolutional neural network (CNN) to extract image features. Two pre-trained CNNs were utilized individually as features extractors: ResNet-50 and VGG-16. All of the experiments were conducted on a portion of the METU test set, equal to 10,000 images, to accelerate the process, along with the injected query set, to evaluate the retrieval performance.