DarwinGSE: Towards better image retrieval systems for intellectual property datasets

• Published in: PloS one Vol. 19; no. 7; p. e0304915
• Main Authors: António, João, Valente, Jorge, Mora, Carlos, Almeida, Artur, Jardim, Sandra
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2024
• Subjects: 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
• DOI: 10.1371/journal.pone.0304915

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.