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

arXiv: https://arxiv.org/abs/2205.05419 · HTML: https://mlsanity.com/paper/2205.05419/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 102 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 18 of 18)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Image Inpainting via Generative Multi-column Convolutional Neural Networks](https://mlsanity.com/paper/1810.08771/) (2018)
- [Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks](https://mlsanity.com/paper/1712.04407/) (2017)
- [Good Practice in CNN Feature Transfer](https://mlsanity.com/paper/1604.00133/) (2016)
- [Open Set Logo Detection and Retrieval](https://mlsanity.com/paper/1710.10891/) (2017)
- [DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer](https://mlsanity.com/paper/1510.02131/) (2015)
- [Deep Learning for Logo Recognition](https://mlsanity.com/paper/1701.02620/) (2017)
- [Component-based Attention for Large-scale Trademark Retrieval](https://mlsanity.com/paper/1811.02746/) (2018)
- [A Large-scale Dataset and Benchmark for Similar Trademark Retrieval](https://mlsanity.com/paper/1701.05766/) (2017)
- [Features Fusion for Classification of Logos](https://mlsanity.com/paper/1609.01414/) (2016)
- [Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval](https://mlsanity.com/paper/1908.02786/) (2019)

## Built on by (most cited first, 2 of 2)

- [Deep Learning for Logo Detection: A Survey](https://mlsanity.com/paper/2210.04399/) (2022)
- [FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings](https://mlsanity.com/paper/2308.09012/) (2023)

## Abstract (arXiv)

Classifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi-class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colors. This work proposes a method that allows visually similar logos to be classified and searched from a set of data according to their shape, color, commercial sector, semantics, general characteristics, or a combination of features selected by the user. Unlike previous approaches, the proposal employs a series of multi-label deep neural networks specialized in specific attributes and combines the obtained features to perform the similarity search. To delve into the classification system, different existing logo topologies are compared and some of their problems are analyzed, such as the incomplete labeling that trademark registration databases usually contain. The proposal is evaluated considering 76,000 logos (7 times more than previous approaches) from the European Union Trademarks dataset, which is organized hierarchically using the Vienna ontology. Overall, experimentation attains reliable quantitative and qualitative results, reducing the normalized average rank error of the state-of-the-art from 0.040 to 0.018 for the Trademark Image Retrieval task. Finally, given that the semantics of logos can often be subjective, graphic design students and professionals were surveyed. Results show that the proposed methodology provides better labeling than a human expert operator, improving the label ranking average precision from 0.53 to 0.68.