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 searc...

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Vydané v:PloS one Ročník 19; číslo 7; s. e0304915
Hlavní autori: António, João, Valente, Jorge, Mora, Carlos, Almeida, Artur, Jardim, Sandra
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 01.07.2024
Predmet:
Algorithms
Biology and Life Sciences
Brand loyalty
Computer and Information Sciences
Copyright infringement
Databases, Factual
Datasets
Descriptions
Digital libraries
Engineering and Technology
Equipment and supplies
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image retrieval
Information retrieval
Information Storage and Retrieval - methods
Intellectual Property
Logos
Medical imaging equipment
Physical Sciences
Queries
Registration
Relevance feedback
Research and Analysis Methods
Retrieval
Searching
Similarity
Social Sciences
Trademarks
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0304915