Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval

Lourenço, Vítor; Silva, Gabriela G.; Fernandes, Leandro A. F.

doi:10.48550/arxiv.1908.02786

Cited by 1 publication

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“…The usage of handcrafted features for this task can also be found in more recent works, such as [16] and [17], which introduce HoVW (Hierarchy of Visual Words), a TIR method that decomposes images into simpler geometric shapes and defines a descriptor for binary logo image representation by encoding the hierarchical arrangement of component shapes. Nonetheless, most recent TIR methods use deep learning [18] architectures.…”

Section: Trademark Image Retrievalmentioning

confidence: 99%

Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features

Bernabeu¹,

Gallego²,

Pertusa³

2022

Preprint

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Logo classification is a particular case of image classification, since these may contain only text, images, or a combination of both. In this work, we propose a system for the multi-label classification and similarity search of logo images. The method allows obtaining the most similar logos on the basis of their shape, color, business sector, semantics, general characteristics, or a combination of such features established by the user. This is done by employing a set of multi-label networks specialized in certain characteristics of logos. The features extracted from these networks are combined to perform the similarity search according to the search criteria established. Since the text of logos is sometimes irrelevant for the classification, a preprocessing stage is carried out to remove it, thus improving the overall performance. The proposed approach is evaluated using the European Union Trademark (EUTM) dataset, structured with the hierarchical Vienna classification system, which includes a series of metadata with which to index trademarks. We also make a comparison between well known logo topologies and Vienna in order to help designers understand their correspondences. The experimentation carried out attained reliable performance results, both quantitatively and qualitatively, which outperformed the state-of-the-art results. In addition, since the semantics and classification of brands can often be subjective, we also surveyed graphic design students and professionals in order to assess the reliability of the proposed method.

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Section: Trademark Image Retrievalmentioning

confidence: 99%