Trademark Background Analysis Method Based on Human Cognition of Visual Psychology

Trademark is a symbol that can be seen everywhere in life. In the case of effective use of trademark, people can quickly identify the organization, its image and reputation, so that the trademark owner can obtain resources for maintenance and development. However, there are unfair competitors in the market that would imitate similar trademarks to steal or damage the reputation and interests of the original trademark owners. Failure of preventing counterfeit trademarks from affecting the interests of others will make developers hard to develop novel technologies that contribute to the progress of the country and society due to insufficient or scarce resources. In the past research in this field,many studies have proposed protection methods for protecting trademark interests. These related studies have obtained excellent results in the search and analysis of trademarks. Nevertheless, some unfair competitors use the human cognition of visual psychology in the human vision system (HVS) to circumvent similar methods of trademark images to steal or damage the credit and interests of the original trademark owners. In order to deter unfair competitors or the abuse of such confusing trademarks, this article proposes a method for trademark background analysis. This article also proposes a method to generate a large amount of background trademark data. Then, we use that large amount of generated data for training neural networks that can analyze trademarks that are easily confused due to background. Eventually, we use the testing independent data for system verification. This experiment uses two common deep learning network architecture for testing. The experimental results show that the proposed system can achieve a true positive rate (TPR) over 98% in the face of this confusing trademark plagiarism method. And the comparison result is better than existing methods. This result confirms that the system proposed in this article can prevent infringement problems that used background.

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.

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.

Translated Trademarks Retrieval using Color Autocorrelogram for Extracted Textual Parts

This article describes a trademark retrieval system using autocorrelogram features. The proposed algorithm deals with the trademarks of a same company having different shapes in different languages. Textual and mixed logos types are considered in this study, in which the text area is located based on an edge detection algorithm and connected components merging strategy to form the bounding box enclosing the textual parts of a logo. Color autocorrelogram is applied on the extracted and quantified text area. An adapted evaluation metric is proposed to measure the performance of the proposed technique. Results show a good retrieval rate on real trademark images.

Novel Relevance Feedback Approach for Color Trademark Recognition Using Optimization and Learning Strategy

The trademark registration process, apparent in all organizations nowadays, deals with recognition and retrieval of similar trademark images from trademark databases. Trademark retrieval is an imperative application area of content-based image retrieval. The main challenges in designing and developing this application area are reducing the semantic gap, obtaining higher accuracy, reducing computation complexity, and subsequently the execution time. The proposed work focuses on these challenges. This paper proposes the relevance feedback system embedded with optimization and unsupervised learning technique as the preprocessing stage, for trademark recognition. The search space is reduced by using particle swam optimization, for optimization of database feature set, which is further followed by clustering using self-organizing map. The relevance feedback technique is implemented over this preprocessed feature set. Experimentation is done using the FlickrLogos-32 PLUS dataset. To introduce variations between the training and query images, transformations are applied to each of the query image, viz. rotation, scaling, and translation of the image. The same query image is tested for various combinations of transformations. The proposed technique is invariant to various transformations, with significant performance as depicted in the results.