Open AccessClothing image classification based on random erasing and residual network
Author(s) -
Zongyan Gao,
Lixin Han
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1634/1/012136
Subject(s) - artificial intelligence , computer science , residual , pattern recognition (psychology) , generalization , clothing , pooling , enhanced data rates for gsm evolution , feature (linguistics) , feature extraction , image (mathematics) , computer vision , data mining , algorithm , mathematics , mathematical analysis , linguistics , philosophy , archaeology , history
The traditional clothing classification method mainly consists of manually extracting obvious features such as color, texture and edge of the image. These artificial feature extraction methods are cumbersome and feature recognition rate is not high. In recent years, Deep Residual Network (ResNet) has been widely used in various fields by increasing network depth to obtain higher recognition accuracy. In this paper, the ResNet model is applied to the classification of clothing images, and on this basis, its data pooling layer is improved so that it can learn more rich features of image data. Clothing images are easy to be deformed and occluded. In this paper, a random erasing data enhancement algorithm is used to integrate and improve the model to improve the generalization ability of the ResNet model to such data. The final experimental results show that the classification accuracy of the improved residual model on clothing data in this paper is improved by 2.43%. At the same time, after integrating the random erasure data enhancement algorithm, the generalization ability of the model has been further improved.