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

Autor: António J; Techframe-Information Systems, SA, São Domingos de Rana, Portugal., Valente J; Techframe-Information Systems, SA, São Domingos de Rana, Portugal., Mora C; Smart Cities Research Center, Polytechnic Institute of Tomar, Tomar, Portugal., Almeida A; Techframe-Information Systems, SA, São Domingos de Rana, Portugal., Jardim S; Smart Cities Research Center, Polytechnic Institute of Tomar, Tomar, Portugal.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Jul 01; Vol. 19 (7), pp. e0304915. Date of Electronic Publication: 2024 Jul 01 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0304915
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 António et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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