Clothing classification method based on convolutional network and attention mechanism

Liefa Liao; Saisai Zhang; Zhiming Li; Pu Yuan; Yiguo Yang

doi:10.1117/12.2637531

15 June 2022 Clothing classification method based on convolutional network and attention mechanism

Liefa Liao, Saisai Zhang, Zhiming Li, Pu Yuan, Yiguo Yang

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122851J (2022) https://doi.org/10.1117/12.2637531
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

To address the problems that existing garment classification effects are greatly affected by background noise and that network features are not highly expressive. A multi-scale deep network model (MCA-Inception) is proposed based on convolutional networks and attention mechanisms. This network model uses the modified Inception V3 as the backbone network and expands the perceptual field by adding convolutional kernels of different scale sizes to enrich the contextual detail information of the garment content. At the same time, the CBAM attention module is embedded in the improved backbone network to suppress the interference of noisy information such as cluttered background and enhance the representation of adequate feature information. Average classification accuracies of 81.63% and 77.80% were obtained on the publicly available clothing datasets DeepFashion and ACS, respectively. Experimental comparisons with other methods show that the proposed network model performs better in the clothing classification task.

Citation Download Citation

Liefa Liao, Saisai Zhang, Zhiming Li, Pu Yuan, and Yiguo Yang "Clothing classification method based on convolutional network and attention mechanism", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122851J (15 June 2022); https://doi.org/10.1117/12.2637531

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available