商标形象通常是消费者与产品或服务之间的第一种间接联系。公司依靠图形商标作为质量和即时识别的象征,寻求保护他们免受侵犯版权的侵害。一种流行的防御机制是图形搜索,将图像与大型数据库进行比较,以查找与类似商标的潜在冲突。尽管不是一个新的主题,最先进的图像检索在工业产权(IP)领域缺乏可靠的解决方案,数据集的内容实际上不受限制,抽象图像,对人类感知建模是一项具有挑战性的任务。现有的基于内容的图像检索(CBIR)系统仍然存在一些问题,特别是在效率和可靠性方面。在本文中,我们提出了一个新的CBIR系统,克服了这些主要的限制。它遵循模块化的方法,由一组负责检索的单个组件组成,维护和逐步优化商标图像搜索,大规模工作,未标记的数据集。它的泛化能力是使用多个特征描述来实现的,单独加权,并组合以表示单个相似性得分。评估图像的一般特征,边缘地图,和感兴趣的地区,采用基于分水K-Means段的方法。我们提出了一种图像恢复过程,该过程依赖于所有特征描述之间的新相似性度量。每天都会添加新的商标图像,以确保最新的结果。所提出的系统展示了及时的检索速度,95%的搜索具有10秒的呈现速度和93.7%的平均精度,支持其对实字IP保护场景的适用性。
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.