Unsupervised Trademark Retrieval Method Based on Attention Mechanism

Cao, Jiangzhong; Huang, Yunfei; Dai, Qingyun; Ling, Wing‐Kuen

doi:10.3390/s21051894

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…Xie Zhuo et al proposed a clustering method based on comparative learning for multi-relational attribute graphs (CCLMAG), which is superior to the current state-of-the-art baseline methods and has practical applications [22]. Cao Yibo et al introduced unsupervised comparative learning into the field of trademark retrieval for the first time, and extracted semantic information through comparative learning to finally improve the average accuracy of trademark retrieval [23]. Joint Public Security of Tsinghua University proposed a loss function based on multi-instance relations to fully exploit the intra-and inter-modal associations between multiple positive and negative samples of masked faces and intact faces to improve the accuracy of masked face recognition [24].…”

Section: Comparative Learningmentioning

confidence: 99%

Contrastive Learning Dimensionality Reduction Method Based on Manifold Learning

Situ

2024

AETR

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With the development of the times, more and more high-dimensional datasets come into people's view. In order to reduce the time complexity and space complexity of downstream tasks, data dimensionality reduction becomes the primary choice. Classical dimensionality reduction algorithms are mainly divided into linear dimensionality reduction algorithms and nonlinear dimensionality reduction algorithms. Some of the traditional dimensionality reduction methods have the problems of not considering the nonlinear structure of the original dataset and the existence of weak generalisation, which makes the dimensionality reduction effect not good or model need to be recalculated because of the addition of new samples. In order to solve these problems, the research in this paper is a comparative learning dimensionality reduction method based on manifold learning. The idea of manifold learning using geodesic distance can fully consider the nonlinear structure of the original dataset. In this paper, comparative learning is the main framework. When the neural network completes the training, it only need to take the new data as input to calculate, the result can be obtained, no need to reconstruct the model which means the generality is high. Starting from the related work, this paper briefly introduces manifold learning, comparative learning and neural network algorithms. Subsequently, an innovative model is proposed, including three modules, Isomap to extract nonlinear structure, expanding neighbourhood to make pseudo-labels, and comparative learning training. Detailed analyses are carried out through experiments, comparing with PCA and LLE algorithms with the geodetic distance retention rate as an indicator, which proves that the data dimensionality reduction method of this model is more effective and ubiquitous.

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Section: Comparative Learningmentioning

confidence: 99%

Contrastive Learning Dimensionality Reduction Method Based on Manifold Learning

Situ

2024

AETR

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“…propose an unsupervised trademark image retrieval system based on attention mechanisms, introducing bias towards relevant features during training. Their system matches or surpasses traditional feature extraction methods and even some supervised systems on the METU trademark dataset [ 115 ]. These last two proposals have their performance evaluation limited to datasets restricted in number of images, as well as their content variability.…”

Section: Related Workmentioning

confidence: 99%

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

António,

Valente,

Mora

et al. 2024

PLoS ONE

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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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“…In [13], the authors considered the possibility of applying the attention mechanism, CNN architecture, and unsupervised learning. The final model used ECANet50 and was trained with instance discrimination.…”

Section: Related Workmentioning

confidence: 99%

Trademark Similarity Evaluation Using a Combination of ViT and Local Features

2023

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The origin of the trademark similarity analysis problem lies within the legal area, specifically the protection of intellectual property. One of the possible technical solutions for this issue is the trademark similarity evaluation pipeline based on the content-based image retrieval approach. CNN-based off-the-shelf features have shown themselves as a good baseline for trademark retrieval. However, in recent years, the computer vision area has been transitioning from CNNs to a new architecture, namely, Vision Transformer. In this paper, we investigate the performance of off-the-shelf features extracted with vision transformers and explore the effects of pre-, post-processing, and pre-training on big datasets. We propose the enhancement of the trademark similarity evaluation pipeline by joint usage of global and local features, which leverages the best aspects of both approaches. Experimental results on the METU Trademark Dataset show that off-the-shelf features extracted with ViT-based models outperform off-the-shelf features from CNN-based models. The proposed method achieves a mAP value of 31.23, surpassing previous state-of-the-art results. We assume that the usage of an enhanced trademark similarity evaluation pipeline allows for the improvement of the protection of intellectual property with the help of artificial intelligence methods. Moreover, this approach enables one to identify cases of unfair use of such data and form an evidence base for litigation.

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Unsupervised Trademark Retrieval Method Based on Attention Mechanism

Cited by 10 publications

References 40 publications

Contrastive Learning Dimensionality Reduction Method Based on Manifold Learning

Contrastive Learning Dimensionality Reduction Method Based on Manifold Learning

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

Trademark Similarity Evaluation Using a Combination of ViT and Local Features

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