Mix Attention Based Convolutional Neural Network for Clothing Brand Logo Recognition and Classification

Over the past years, fashion clothing related research problems such as clothing style classification, clothing attribute recognition, clothing recommendation, and clothing retrieval, have received great attention in the society of computer vision, pattern recognition and image processing. Recently,...

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Bibliographic Details
Published in:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 3013 - 3018
Main Authors: Liu, Kuan-Hsien, Chen, Guan-Hong, Liu, Tsung-Jung
Format: Conference Proceeding
Language:English
Published: IEEE 17.10.2021
Subjects:
Attention module
clothing brand recognition
clothing classification and retrieval
Computer vision
Conferences
deep convolutional neural network
Deep learning
fashion clothing brand dataset
Focusing
Image recognition
Pattern recognition
ISSN:2577-1655
Online Access:Get full text
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Summary:Over the past years, fashion clothing related research problems such as clothing style classification, clothing attribute recognition, clothing recommendation, and clothing retrieval, have received great attention in the society of computer vision, pattern recognition and image processing. Recently, approaches based on deep learning have been proposed to tackle with aforementioned problems. However, no research work focusing on clothing brand identification/recognition is investigated. To deal with clothing brand identification problem, we construct a new very large-scale clothing dataset containing brand information and the brand logo bounding box information if it has one. Totally, we collected 416 fashion clothing brands in our newly built dataset. In this work, we propose a fashion Clothing Brand Recognition Network (CBR-Net), which contains two parts. First part is the identification network for the clothing brand logo detection and recognition, and the second part is an attention module based network for classifying clothing brands without logos. In the experiments, we demonstrated that our newly proposed CBR-Net can attain much better performance than other state-of-the-art methods.
ISSN:2577-1655
DOI:10.1109/SMC52423.2021.9658730