An intelligent content-based image retrieval methodology using transfer learning for digital IP protection

Trappey, Amy J.C.; Trappey, Charles V.; Shih, Samuel P.

doi:10.1016/j.aei.2021.101291

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…Existing research in automatic trademark IP infringement detection typically simplify the problem definition to logo image similarity (Peng and Chen, 1997;Alshowaish et al, 2022;Trappey et al, 2020;Li et al, 2023;Mao et al, 2023;Trappey et al, 2021a;Tursun et al, 2019) or textual similarity detection (Trappey et al, 2020), subsequently proposing methods using diverse machine learning models like convolutional neural networks (Gu et al, 2018) or recurrent neural networks (Hochreiter and Schmidhuber, 1997). Other studies delved into constructing trademark ontologies (Trappey et al, 2021b) or developing logo similarity detection datasets (Hou et al, 2021;.…”

Section: Related Workmentioning

confidence: 99%

TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce

Hu,

Li,

Jin

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

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Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms. However, the absence of high-quality datasets hampers research in this area. To address this gap, our study introduces TMID, a novel dataset to detect trademark infringement in merchant registrations. This is a real-world dataset sourced directly from Alipay, one of the world's largest e-commerce and digital payment platforms. As infringement detection is a legal reasoning task requiring an understanding of the contexts and legal rules, we offer a thorough collection of legal rules and merchant and trademark-related contextual information with annotations from legal experts. We ensure the data quality by performing an extensive statistical analysis. Furthermore, we conduct an empirical study on this dataset to highlight its value and the key challenges.

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Section: Related Workmentioning

confidence: 99%

TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce

Hu,

Li,

Jin

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

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“…CBIR differs from other image retrieval systems because it accepts images as input rather than tags or text [10]. Numerous image retrieval techniques concentrate on low-level image characteristics such as textures, shapes, and colors [11]. Every CBIR system is typically divided into offline and online phases [12].…”

Section: Content Based Image Retrieval (Cbir)mentioning

confidence: 99%

An Overview of Content-Based Image Retrieval Methods and Techniques

Hadid,

Hussein,

Al-Qaysi

et al. 2023

ijcsm

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With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval

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“…Charles et al [54] introduced a local mesh color texture pattern (LMCTP), which uses color and spatial features and merges them to create a local descriptor. Amy et al [55] used the transfer learning approach to find the TM logo similarity used for images retrieval. Christy et al [56] proposed new methodology for CBIR, which extract pixels information from an image based on its shape, attributes, and tag.…”

Section: State Of the Artmentioning

confidence: 99%

PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

et al. 2022

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This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

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An intelligent content-based image retrieval methodology using transfer learning for digital IP protection

Cited by 20 publications

References 22 publications

TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce

TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce

An Overview of Content-Based Image Retrieval Methods and Techniques

PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

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