Paper Reading AI Learner

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

2022-05-11 11:40:40
Marisa Bernabeu, Antonio Javier Gallego, Antonio Pertusa

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.

Abstract (translated)

URL

https://arxiv.org/abs/2205.05419

PDF

https://arxiv.org/pdf/2205.05419.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot