A CNN Based Approach for Garments Texture Design Classification

Authors

  • S.M. Sofiqul Islam Institute of Information Technology, University of Dhaka, Bangladesh
  • Emon Kumar Dey Institute of Information Technology, University of Dhaka, Bangladesh
  • Md. Nurul Ahad Tawhid Institute of Information Technology, University of Dhaka, Bangladesh
  • B. M. Mainul Hossain Institute of Information Technology, University of Dhaka, Bangladesh

Keywords:

CNN, deep learning, AlexNet, VGGNet, texture de-scriptor, garment categories, garment trend identification, design classification for garments

Abstract

Identifying garments texture design automatically for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several Hand-Engineered feature coding exists for identifying garments design classes. Recently, Deep Convolutional Neural Networks (CNNs) have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data more accurately. In this paper, a CNN model for identifying garments design classes has been proposed. Experimental results on two different datasets show better results than existing two well-known CNN models (AlexNet and VGGNet) and some state-of-the-art Hand-Engineered feature extraction methods.

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Published

2017-05-09

How to Cite

[1]
S. S. Islam, E. K. Dey, M. N. A. Tawhid, and B. M. M. Hossain, “A CNN Based Approach for Garments Texture Design Classification”, Adv. technol. innov., vol. 2, no. 4, pp. 119–125, May 2017.

Issue

Vol. 2 No. 4 (2017): October

Section

Articles

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