Trademark Images Research Articles

Trademark Text Recognition Combining SwinTransformer and Feature-Query Mechanisms

The task of trademark text recognition is a fundamental component of scene text recognition (STR), which currently faces a number of challenges, including the presence of unordered, irregular or curved text, as well as text that is distorted or rotated. In applications such as trademark infringement detection and analysis of brand effects, the diversification of artistic fonts in trademarks and the complexity of the product surfaces where the trademarks are located pose major challenges for relevant research. To tackle these issues, this paper proposes a novel recognition framework named SwinCornerTR, which aims to enhance the accuracy and robustness of trademark text recognition. Firstly, a novel feature-extraction network based on SwinTransformer with EFPN (enhanced feature pyramid network) is proposed. By incorporating SwinTransformer as the backbone, efficient capture of global information in trademark images is achieved through the self-attention mechanism and enhanced feature pyramid module, providing more accurate and expressive feature representations for subsequent text extraction. Then, during the encoding stage, a novel feature point-retrieval algorithm based on corner detection is designed. The OTSU-based fast corner detector is presented to generate a corner map, achieving efficient and accurate corner detection. Furthermore, in the encoding phase, a feature point-retrieval mechanism based on corner detection is introduced to achieve priority selection of key-point regions, eliminating character-to-character lines and suppressing background interference. Finally, we conducted extensive experiments on two open-access benchmark datasets, SVT and CUTE80, as well as a self-constructed trademark dataset, to assess the effectiveness of the proposed method. Our results showed that the proposed method achieved accuracies of 92.9%, 92.3% and 84.8%, respectively, on these datasets. These results demonstrate the effectiveness and robustness of the proposed method in the analysis of trademark data.

DarwinGSE: Towards better image retrieval systems for intellectual property datasets.

A trademark's image is usually the first type of indirect contact between a consumer and a product or a service. Companies rely on graphical trademarks as a symbol of quality and instant recognition, seeking to protect them from copyright infringements. A popular defense mechanism is graphical searching, where an image is compared to a large database to find potential conflicts with similar trademarks. Despite not being a new subject, image retrieval state-of-the-art lacks reliable solutions in the Industrial Property (IP) sector, where datasets are practically unrestricted in content, with abstract images for which modeling human perception is a challenging task. Existing Content-based Image Retrieval (CBIR) systems still present several problems, particularly in terms of efficiency and reliability. In this paper, we propose a new CBIR system that overcomes these major limitations. It follows a modular methodology, composed of a set of individual components tasked with the retrieval, maintenance and gradual optimization of trademark image searching, working on large-scale, unlabeled datasets. Its generalization capacity is achieved using multiple feature descriptions, weighted separately, and combined to represent a single similarity score. Images are evaluated for general features, edge maps, and regions of interest, using a method based on Watershedding K-Means segments. We propose an image recovery process that relies on a new similarity measure between all feature descriptions. New trademark images are added every day to ensure up-to-date results. The proposed system showcases a timely retrieval speed, with 95% of searches having a 10 second presentation speed and a mean average precision of 93.7%, supporting its applicability to real-word IP protection scenarios.

Multi‐label logo recognition and retrieval based on weighted fusion of neural features

AbstractClassifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi‐class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colours. This work proposes a method that allows visually similar logos to be classified and searched from a set of data according to their shape, colour, commercial sector, semantics, general characteristics, or a combination of features selected by the user. Unlike previous approaches, the proposal employs a series of multi‐label deep neural networks specialized in specific attributes and combines the obtained features to perform the similarity search. To delve into the classification system, different existing logo topologies are compared and some of their problems are analysed, such as the incomplete labelling that trademark registration databases usually contain. The proposal is evaluated considering 76,000 logos (seven times more than previous approaches) from the European Union Trademarks dataset, which is organized hierarchically using the Vienna ontology. Overall, experimentation attains reliable quantitative and qualitative results, reducing the normalized average rank error of the state‐of‐the‐art from 0.040 to 0.018 for the Trademark Image Retrieval task. Finally, given that the semantics of logos can often be subjective, graphic design students and professionals were surveyed. Results show that the proposed methodology provides better labelling than a human expert operator, improving the label ranking average precision from 0.53 to 0.68.

The Influence of Green Marketing and Brand Awareness on Purchase Intention Through Brand Image as a Mediation Variable

Marketing using the concept of green marketing should create positive emotions so that consumers can influence product evaluation. Objectives: The problems of the study are: Does green marketing influence brand image? Does green marketing influence buying interest through brand image as a mediation variable? Does brand awareness influence buying interest? Does brand awareness influence brand image? Does brand awareness influence purchasing attention through the perception of a brand as a mediation variable? Does brand image influence buying interest? Meanwhile, the objectives of the study are to find out the influence of green marketing on buying interest, the influence of green marketing on brand image, the influence of green marketing on buying interest through brand image as a mediation variable, the brand awareness's impact on consumers' purchase intentions, the impact of brand awareness on brand image, the influence of buying interest through brand image as a mediation variable, and the influence of brand image on buying interest.Methodology: This research is part of exploration research. Participants in the research are unknown; the quantity of samples used in this research is 110. The analysis tool used is PLS.Finding: green marketing does not influence buying interests; green marketing does not influence brand image; brand image does not moderate the impact of green advertising on buying interests; brand awareness influences buying interests; brand awareness influences brand image; brand image does not mediate the impact of brand recognition on purchasing interests; and trademark image influences purchasing interests. Conclusion: Based on the findings from the conclusions of this study, these are some suggestions that can be used as research recommendations: this research focuses on the variables green marketing, brand awareness, brand image, and buying interest, so other variables such as brand trust, social media marketing, perceived quality, price, and customer satisfaction are needed to find out the buying interest; Future studies are anticipated to increase the sample size, expand the research object, and add relevant topics; future research is expected to process data with different analysis tools.

Cross-Checking-Based Trademark Image Retrieval for Hot Company Detection

A trademark is an essential symbol of a company, consisting of a semantically rich image under ordinary circumstances. The popularity of a company can be measured by the frequency of its trademark being used. Therefore, efficiently retrieving trademark images would directly contribute to the detection of popular companies. However, most mainstream retrieval methods are not especially pertinent to trademark image retrieval. To solve this problem, a combination of the ResNet50 network and Autoencoder with local sensitive hashing (LSH) is used to conduct full cross-checking, which significantly improves the effectiveness of trademark image retrieval. Meanwhile, image super-resolution-based sparse coding is also proposed to achieve high-precision trademark image retrieval and its effect is particularly significant for challenging trademark images. Finally, the authors conduct extensive experiments on a high-quality database to demonstrate the substantial effectiveness of the proposed methods.

A Novel Trademark Image Retrieval System Based on Multi-Feature Extraction and Deep Networks.

Graphical Search Engines are conceptually used in many development areas surrounding information retrieval systems that aim to provide a visual representation of results, typically associated with retrieving images relevant to one or more input images. Since the 1990s, efforts have been made to improve the result quality, be it through improved processing speeds or more efficient graphical processing techniques that generate accurate representations of images for comparison. While many systems achieve timely results by combining high-level features, they still struggle when dealing with large datasets and abstract images. Image datasets regarding industrial property are an example of an hurdle for typical image retrieval systems where the dimensions and characteristics of images make adequate comparison a difficult task. In this paper, we introduce an image retrieval system based on a multi-phase implementation of different deep learning and image processing techniques, designed to deliver highly accurate results regardless of dataset complexity and size. The proposed approach uses image signatures to provide a near exact representation of an image, with abstraction levels that allow the comparison with other signatures as a means to achieve a fully capable image comparison process. To overcome performance disadvantages related to multiple image searches due to the high complexity of image signatures, the proposed system incorporates a parallel processing block responsible for dealing with multi-image search scenarios. The system achieves the image retrieval through the use of a new similarity compound formula that accounts for all components of an image signature. The results shows that the developed approach performs image retrieval with high accuracy, showing that combining multiple image assets allows for more accurate comparisons across a broad spectrum of image typologies. The use of deep convolutional networks for feature extraction as a means of semantically describing more commonly encountered objects allows for the system to perform research with a degree of abstraction.

Graphical Image Region Extraction with K-Means Clustering and Watershed.

With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

Intelligent trademark recognition and similarity analysis using a two-stage transfer learning approach

The ubiquitous and fast-growing e-marketplaces are causing serious concerns of unauthorized trademark usages, especially their pictorial images. There is a significant need for e-commerce service intermediaries and trading sites to actively check whether product images posted online are not infringing upon others’ intellectual property rights (IPRs). To ensure registered trademark (TM) or copyrighted logo-image (logo) protection, this research develops an intelligent system that can detect, locate, and crop (cut) logos posted online and check whether they are substantially or deceptively similar to registered TM logos. This research integrates two deep learning models to achieve the research goal. The first is the logo detection and localization model for cropping trademark like images from complex online merchandise photos, which could have images of many product views and even worn and displayed on a human model. Only the cropped logo image is needed for comparison to a database of registered trademarks. The second model performs TM similarity analysis using the cropped logos compared to the published TM images. Yolo v4 is adopted as the general logo locator for intelligent logo image cropping. The triplet convolutional neural network model is used to fine-tuned for intelligent trademark similarity analysis. The models are trained with an image dataset, combining image samples from LogoDet-3k and images found through web search. The system performance is quantitatively tested using the FlickrLogos-32 dataset and consumer product images extracted from an e-commerce platform. The test results of the system achieve high precision (0.91) for trademark class matching.

Competition law in attempt to understand (Luxury) trademarks

ABSTRACT The paper analyses the legality of online sales bans in selective distribution agreements in the EU, focusing on the “luxury brand image” justification as per the CJEU judgements and the decisions of the EC and national competition authorities. The paper questions the criteria for determining what constitutes a luxury trademark, and whether it is fair to distinguish (or even discriminate) between luxury and non-luxury trademarks, considering that both should have effective control and choice of the distribution of their goods. The paper concludes that the success of the selective distribution system depends on the effective control the trademark owner exercises over it, irrespective of whether or not it is covered by the aura of luxury. The non-luxury brands currently may justify their restrictions on sales on third party platforms under the unfair competition grounds. The selective distribution system should aim at protecting trademark image, which may not necessarily be luxury.

Text Detection in Trademark Images under Semi-Supervision

The texts in trademark image usually contain characters, text lines and other different parts. Therefore, compared with the detectors that can detect text lines directly, the detector that can detect characters is more suitable for the detection of texts in trademark images However, training character detectors needs a huge number of characters with position annotation, the con-struction process of this type of dataset will take a lot of time. In order to overcome the limitations of the detectors trained by datasets with word-level annotations and the shortage of datasets with character-level annotations, we propose a text detect method that can detect the single character in trademark images. Our proposed model is pretrained by the synthetic image dataset with character level annotation, then the pretrained model is used to detect the unannotated trademark images dataset to find more annotations for retrain the pretrained model. We also construct a trademark text dataset, which includes 4500 images. Experimental results on our dataset demonstrate our method can achieve the state-of-the-art performance compared to other methods. The results show our detector can detect complicated texts in trademark images.

A shape and texture features fusion to retrieve similar Trademark Image Material

Trademark images or materials such as symbols, text, logos, image, design or phrase are used to unique representation of any organization. Retrieval of trademark material images are important to protect the new trademark image that is to be registered. Therefore, retrieval of similar trademark images is required. In this paper, an approach is presented to extract more similar trademark images so that a unique trademark image can be registered. In this paper, Zernike moment of the query image and dataset images are computed, then most similar images from the dataset are retrieved at the first layer refinement. In the second layer, texture features are extracted of query image and refined dataset images to retrieve most appropriate similar images. Zernike moments is applied to extract global shape features and Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Feature (SURF) are applied to extract texture features on the basis of a few key-points of the trademark images. A weighted average of both the key-points feature vectors is computed for retrieving the rank1, rank5, rank10, rank15 and rank20 most similar images using Euclidean distance. Experiments have been performed on a proposed dataset to perform the analysis and found that proposed work perform better and improves the accuracy.

Recent developments of content-based image retrieval (CBIR)

With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR and many effective CBIR algorithms have been established. In this paper, we present a survey on the fast developments and applications of CBIR theories and algorithms during the period from 2009 to 2019. We mainly review the technological developments from the viewpoint of image representation and database search. We further summarize the practical applications of CBIR in the fields of fashion image retrieval, person re-identification, e-commerce product retrieval, remote sensing image retrieval and trademark image retrieval. Finally, we discuss the future research directions of CBIR with the challenge of big data and the utilization of deep learning techniques.

Efficient retrieval of trademark images from large databases

Trademarks law is considered as the most pervasive amongst all the intellectual property laws because all the judgments require contemplating the imaginations of the consumers. Once the trademark has been submitted for registration, the examiners at the trademark office make sure that it is not similar to any of the previous registered trademarks. This motivated the need of an automated trademark retrieval system. This paper makes a contribution in the field of trademark image retrieval by proposing a retrieval technique that allows a flexible combination of colour, texture and shape features. Moreover the proposed technique utilises HSV colour histogram, for colour, multi resolution Gabor wavelet for texture and an integration of Zernike moments for global shape and scale invariant feature transform (SIFT) for local shape feature extraction. The results have been tested on MPEG7, MPEG trademark, WANG and self-compiled datasets. The improvement achieved in precision is 14% on MPEG7, 30% on the WANG and 38% on self-compiled dataset. Similarly, 26% improvement in average recall is achieved and around 16% when our proposed shape feature is compared with other state of the art techniques like Fourier descriptors, Hu moments, wavelet descriptors, Zernike moments and edge gradient co-occurrence matrix.

Efficient retrieval of trademark images from large databases

Efficient retrieval of trademark images from large databases

Building national identity in opposition to otherness in liberal Age. Racial prejudices in trademark images in Central State Archives

The paper analyses the origins of Italian national identity in opposition to the «otherness» of the African peoples subject to colonization between the end of the 19th century and the 1920s. The paper takes into consideration background studies in the history of pedagogy, among which, Gabrielli (2013, 2015) and colonial studies as Del Boca (1988) and Labanca (2002) in order to investigate the development of racial stereotypes outside the school. Racial stereotyping increased in advertising and emerged in trademark images of Italian companies so that it influenced the idea of otherness between 1890 – i.e. the conquest of Eritrea – and 1922 – i.e. the advent of Fascism.

Pazarlama Yönetiminde Markalar

Marka, bir işletmenin mal ve hizmetlerini bir başka işletmenin mal ve hizmetlerinden ayırt eder. Pazarlama yönetimi araçlarından olan markalar, müşteri zihinlerinde ürünlere ilişkin bir imaj oluşturur. Ürünlerin piyasa fiyatı, marka imajı ile oluşan değeri de içerir. Pazarlama açısından, marka değerine odaklanan birçok çalışma vardır. Küresel ortamda pazarlama stratejileri geliştirmede, markalaşma çabaları önem kazanmıştır. İşletmeler için sürdürülebilir rekabet avantajı sağlamada, marka oluşturma ve marka koruması önemli hale gelmiştir. Ülkeler açısından da, rekabet avantajı sağlamada yine marka oluşturmanın ve korumanın önemli rolü vardır. Ülkelerin rekabet gücünde önemli göstergeler olan ihracat/ milli gelir büyüklükleri ile marka oluşturma/koruma faaliyetleri arasında genelde benzer bir eğilim vardır. İşletmeler açısından marka oluşturma ve marka koruması, sürdürülebilir rekabet avantajı sağlayarak daha fazla getiri elde etmede vazgeçilmez öneme sahiptir.

Indexing Techniques in Trademark Image Retrieval Systems

The development of automatic trademark image retrieval systems becomes a necessity because of the increasing number of registered trademarks in all countries. The goal is to protect the registered trademarks from counterfeiting and infringement. This paper introduces a trademark image retrieval system using indexing techniques. The proposed system is described by giving an overview about its architecture and describing in details all its components. The goal is to allow researchers and developers in image retrieval to build their own trademark retrieval system using the indexing techniques. Each part of the proposed system is considered as a component that can be improved or replaced. The reader can have a clear idea on: (1) the type of visual features to extract from the trademark images, (2) the indexing technique that can be used to organize the extracted features and speed-up the search and (3) how to perform a similar search for a new trademark image. The proposed system has been evaluated using several global features and the best performance is obtained when using Zernike moments coefficients with order 12.

Trademark Image Retrieval Based on Faster R-CNN

Automatic retrieval of digital trademark images is significant for improving the efficiency of trademark examination and management. In this paper, we proposed a method based on deep learning for trademark retrieval. Faster R-CNN was first applied to trademark retrieval. The global feature descriptor of the image is extracted by Faster R-CNN and the local feature of the image is extracted through the object proposal regions by the Region Proposal Networks (RPN). Retrieval strategies consist of initial ranking and spatial reranking. The experimental results show that our proposed method achieves remarkable performance in trademark image retrieval.

Trademark image retrieval via transformation-invariant deep hashing

Trademark images are usually used to distinguish goods due to their uniqueness, and the amount becomes too huge to search these images accurately and fast. Most existing methods utilize conventional dense features to search visually-similar images, however, the performance and search time are not satisfactory. In this paper, we propose a unified deep hashing framework to learn the binary codes for trademark images, resulting in good performance with less search time. The unified framework integrates two types of deep convolutional networks (i.e., spatial transformer network and recurrent convolutional network) for obtaining transformation-invariant features. These features are discriminative for describing trademark images and robust to different types of transformations. The two-stream networks are followed by the hashing layer. Network parameters are learned by minimizing a sample-weighted loss, which can leverage the hard-searched images. We conduct experiments on two benchmark image sets, i.e., NPU-TM and METU, and verify the effectiveness and efficiency of our proposed approach over state-of-the-art.

A Novel Shape Representation Method for Complex Trademark Image

Trademark orthogonal representation is an important research field in artificial intelligence technology and has extensive applications in trademark retrieval and recognition. However, the Gibbs phenomenon arises when complex trademark shapes are represented by using traditional methods, such as continuous orthogonal function and wavelet. In order to represent the complex trademark without Gibbs phenomenon, a novel method named hierarchical V-system (HV system) is proposed in this study, which is generated by V-system of multi-degree $k$ ( $k = 0, 1, 2, 3$ ). The hierarchical structure brings more detailed shape representation information. We show that the proposed method in this paper can represent the trademark shapes with fewer finite terms and obtain accurate representation result. This study also proposes the normalized descriptors of hierarchical V-system (HV descriptors). Furthermore, we demonstrate that the HV descriptors satisfy the invariance in rotation, translation, and scale transform. The experimental results show that the hierarchical V-system method can give reasonable descriptors for representing complex trademark without Gibbs phenomenon.