A Conceptual Model of Trademark Retrieval based on Conceptual Similarity

Anuar, Fatahiyah Mohd; Setchi, Rossitza; Lai, Yu‐Kun

doi:10.1016/j.procs.2013.09.123

Cited by 9 publications

(12 citation statements)

References 11 publications

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“…The proposed retrieval algorithm is based on a conceptual model of the trademark comparison process developed in the authors' previous work [41]. It provides a bird's eye view of trademark comparison based on conceptual similarities.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…The mapping function is designed so that the trademark similarity distance computation is performed only on the set of trademarks that consist of at least one of the terms in f s , i.e., the synonyms set belonging to the trademark query. The rationale for this mapping function is based on the analysis performed on the acquired infringement cases, as discussed in [41]. The final indexing table is merely a table that maps each trademark in the database to a set of trademarks from the same database for the trademark similarity computation.…”

Section: )mentioning

confidence: 99%

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Semantic Retrieval of Trademarks Based on Conceptual Similarity

Anuar

Setchi

Lai

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. The paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky's theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1,400 disputed cases and a database of 378,943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the R-precision score, which is commonly used in information retrieval, and human judgment/collective human opinion, which is used in human-machine systems.

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Section: The Proposed Algorithmmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Semantic Retrieval of Trademarks Based on Conceptual Similarity

Anuar

Setchi

Lai

2016

IEEE Trans. Syst. Man Cybern, Syst.

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“…The proposed system is provided good precision compare to regular search engine that is an simple and well powerful system. F. M. Anuar, R. Setchi, and Y. K. Lai [9], to mainly focus on main fact by proposing a notation flow of the different procedure, to main at reflow the same trademarks. To compute notation between trademarks use the natural language procedure and same processing.…”

Section: Related Workmentioning

confidence: 99%

“…A conceptual model of trademark retrieval based on conceptual similarity [9] Paper proposed to mainly focus on main fact by proposing a notation flow of the different procedure, to main at reflow the same trademarks…”

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confidence: 99%

Study of Semantic Retrieval by Data Similarity of Trademarks

2015

IJSR

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A trademarks is a sign that you can use to distinguish your business goods or services from those of other traders.Trademark can be defined expressly in the form of any symbol, logo,titles etc. so, they need to be secure. This paper deciphers the hypothetic similarities among trademarks, which happens when more than two or more trademarks hail equal or relevant semantic implant. The state-of-the-art by offering a semantic algorithm to similitude trademarks in preconditions of hypothetic parallelism. By using Tversky's theory similarity, it is derived that search and indexing technique developed similarity distance. The offered reflow algorithm is confirmed using two resources: a trademark database of conflicting cases and a databases company names. Use the different domains to measures the accuracy of the algorithm which gathered different data.

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“…In searching for similar shapes, work [7] regards high-frequency features as sketched images, and then uses the combination of grids as features to search for related images. Some works such as [8] and [9] used Tversky's similarity theory to search for the similarity between trademarks based on the semantic combination in trademarks. With the rise of deep learning today, some works utilized convolutional neural network (CNN) to recognize different types of trademarks from the past to the present [10], or classify trademarks by countries and region [11].…”

Section: Introductionmentioning

confidence: 99%

Trademark Background Analysis Method Based on Human Cognition of Visual Psychology

Hung

Chen

2020

Preprint

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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.

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A Conceptual Model of Trademark Retrieval based on Conceptual Similarity

Cited by 9 publications

References 11 publications

Semantic Retrieval of Trademarks Based on Conceptual Similarity

Semantic Retrieval of Trademarks Based on Conceptual Similarity

Study of Semantic Retrieval by Data Similarity of Trademarks

Trademark Background Analysis Method Based on Human Cognition of Visual Psychology

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