Fabric Classification and Matching Using CNN and Siamese Network for E-commerce

Abstract

According to the Google-Kearney study (May 2016), the fashion industry in India has a tremendous scope and can easily surpass the electronic consumer product sector as early as 2020. However, the apparel sector faces a major limitation on the part of the subjectivity in judging the fabric quality. There is no doubt that the e-commerce industry can earn the highest rate of return from the apparel sector; still, its popularity often got limited. Any person purchasing apparel always first like to touch the fabric to get a ‘feel’ of the fabric and its texture to compare it with the mental/latent representation of other fabrics to assess the quality or equivalence. Though the ‘feel’ of any fabric texture cannot be physically quantified, the latent representation of fabric texture can be extracted and compared using Autoencoders and Siamese networks respectively. In this paper, we have utilized an inexpensive (less than \(5\%\) frugal cellular microscope for the data collection in contrast to any expensive fabric texture scanners. We have utilized Convolutional Neural Networks based Autoencoders and Siamese network for classification, clustering, and matching of similar fabric textures. We have shown that even with frugal data collection methods, the proposed CNN classifiers using the latent feature representation of fabric texture gives a higher accuracy of \(98.40\%\) for fabric texture classification.