Clothing comfort evaluation based on transfer learning and support vector machine

The traditional methods for clothing comfort evaluation is carried out through the try-on effect of the garment, which requires much time but with low evaluation accuracy. This paper presented a clothing comfort evaluation model learning from clothing patterns based on the transfer learning and support vector machine fast and accurately. The sizes of mannequins and the graphs of garment patterns were firstly collected, and the graphs of garment patterns were improved by using transfer learning to create garment pattern database. Then, a comfort label acquisition method was presented based on Virtual Try-On, adding comfort label to the corresponding graph of garment pattern. Following that, local binary pattern was extracted from the graph of garment pattern, and it was combined with the sizes of the corresponding mannequins to form clothing comfort feature vector. Finally, the clothing comfort feature vectors of garment pattern database were extracted to train the support vector machine. This exercise shows that the accuracy and average time to evaluate clothing comfort using this method are 0.834 and 12 s respectively, representing satisfactory accuracy and efficiency. Copyright No content may be reproduced or abridged without authorization.