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

• URL: http://arxiv.org/abs/2205.05419v1
• Date: Wed, 11 May 2022 11:40:40 GMT
• Title: Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features
• Authors: Marisa Bernabeu, Antonio Javier Gallego, Antonio Pertusa
• Abstract summary: 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. The proposed approach is evaluated using the European Union Trademark (EUTM) dataset.
• Score: 6.6144185930393435
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition [21.36538164675385]
  Multi-label image recognition aims to predict a set of labels that present in an image. In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem. We propose an innovative Semantic-aware Graph Matching framework for Multi-Label image recognition (ML-SGM)
  arXiv  Detail & Related papers  (2023-04-21T23:48:01Z)
• Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [70.36722026729859]
  We propose a dual-perspective semantic-aware representation blending (DSRB) that blends multi-granularity category-specific semantic representation across different images. The proposed DS consistently outperforms current state-of-the-art algorithms on all proportion label settings.
  arXiv  Detail & Related papers  (2022-05-26T00:33:44Z)
• Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels [70.45813147115126]
  Multi-label image recognition with partial labels (MLR-PL) may greatly reduce the cost of annotation and thus facilitate large-scale MLR. We find that strong semantic correlations exist within each image and across different images. These correlations can help transfer the knowledge possessed by the known labels to retrieve the unknown labels.
  arXiv  Detail & Related papers  (2022-05-23T08:37:38Z)
• Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [86.17081952197788]
  We propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels. Experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors.
  arXiv  Detail & Related papers  (2022-03-04T07:56:16Z)
• Contextual Similarity Aggregation with Self-attention for Visual Re-ranking [96.55393026011811]
  We propose a visual re-ranking method by contextual similarity aggregation with self-attention. We conduct comprehensive experiments on four benchmark datasets to demonstrate the generality and effectiveness of our proposed visual re-ranking method.
  arXiv  Detail & Related papers  (2021-10-26T06:20:31Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• Zero-Shot Recognition through Image-Guided Semantic Classification [9.291055558504588]
  We present a new embedding-based framework for zero-shot learning (ZSL) Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification.
  arXiv  Detail & Related papers  (2020-07-23T06:22:40Z)
• Hierarchical Image Classification using Entailment Cone Embeddings [68.82490011036263]
  We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier. We empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance.
  arXiv  Detail & Related papers  (2020-04-02T10:22:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.