# Trademark Image Similarity Detection Using Convolutional Neural Network
## 2 Related Work
The authors highlighted some cases in which the proposed method was inefficient. The hard attention mechanism accurately identified the critical components in the images compared with soft attention. [PERSON] et al. [14] introduced a search engine for trademark similarity detection based on AI. The proposed approach simultaneously searches for similarity among four different aspects: content similarity, image similarity, text similarity, and manually defined content similarity. Then, [PERSON] et al. 's approach allowed the examiners to do a manual check between the input trademark and retrieved images from each aspect. As a result, the authors concluded that separating the search aspects produces high detection accuracy when combined with the manual examination. For the same purpose, [PERSON] et al. [6] proposed a search approach for trademark image similarity. The feature vectors of trademark images were extracted using ResNet-v2, a pretrained CNN. Then, these vectors were used to search for the nearest image neighbor in the database using the Approximate Nearest Neighbor Oh Yeah and NearPy libraries. The USPTO trademark dataset was used for the model training, whereas the simplistic test dataset was used for model testing purposes. The results obtained a score of 0.69 in mAP. Searching among large trademark datasets takes a long time and has a high memory cost. [PERSON] et al. [15] proposed a framework based on deep hashing to classify trademark images for retrieval purposes. They applied a data augmentation strategy to the dataset's images to enhance the training process. They used two heterogeneous convolutional networks to extract the visual features: a spatial transformer network and a recurrent convolutional network. A fusion layer was added in addition to a hash layer. This combined both networks' feature maps to generate binary code for the images. The Hamming distances metric was used to measure the similarity between the query image and each image stored in the dataset. The proposed framework was evaluated using two trademark image datasets: NPU-TM and METU. The obtained results were 0.449 and 0.501 mAP using the NPU-TM and METU datasets, respectively. [PERSON] et al. [16] proposed another method for trademark-similarity retrieval using vector images. The proposed method extracted the features of each object in a given image from the image's angle histogram. Then, the algorithm evaluated the objects' similarity between a query image and images in the dataset by measuring the distance. The proposed method independently evaluated the shape similarities between each object well. Since the histogram angle does not depend on the location of the segment, [PERSON] et al. [17] proposed an enhancement to the method in [16] by considering the relative location of contour segments besides their angle. Thus, contour points were prepared and these defined the shape features as two vectors for each object. The first vector is the distance (d) between the centroid point and every contour point, and the second is the angle between the line of d and tangent line of the object at the contour point. Results showed that the accuracy of the similarity retrieval method was improved. The combination of text, pronunciation, and image also represents the trademark and must protect it against infringement. [PERSON] et al. [3] proposed a novel system to analyze the trademark similarity that involves spelling, pronunciation, and image similarity. The system contains three machine learning models to study three types of similarities: natural language processing (NLP), SNN, and CNNs. The Word2 vec model was constructed using Google News to analyze spelling similarity, and then the string similarity was measured with the vector space model algorithm. In contrast, the pronunciation similarity was detected using the phonetic matching algorithm, and its similarity was then measured with the Levenshtein algorithm. The proposed method used a combination of CNN and SNN for image-based similarity, which is our interest.