# Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network
## 5 Conclusions  
We have proposed a biologically-inspired model of joint processing for the extraction of fabric texture and yarn color information based on known properties of the primate visual cortex, which is capable of recognizing and classifying woven fabric textures (weave pattern) invariantly from the color image. Once the algorithm is trained for all the fabric weave patterns (textures) and yarn colors manufactured by a fabric manufacturer, the algorithm will provide an automated recognition and classification of the texture to facilitate the process of sorting the fabric with respect to color and pattern.  
We have tested the algorithm to recognize three different types of changes in the fabric texture, and have shown that the proposed descriptor performs on par, or better than the existing algorithms. We found that the recognition accuracy is significant with an average accuracy rate of 97.5%. It should be noted that although we used shirt fabrics to test our algorithm, the algorithm can also be used on other apparel or clothes, like towels or bed sheets.  
We further found that the algorithm can also be used to recognize a particular texture woven over a fabric. The developed algorithm may also be trained to recognize and correctly classifying  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Predicted Class**} \\ \hline  & & **Pattern 1** & **Pattern 2** & **Pattern 3** & **Pattern 4** \\ \hline \multirow{3}{*}{**Actual Class**} & **Pattern1** & 390 & 3 & 2 & 5 \\  & **Pattern2** & 2 & 390 & 7 & 1 \\  & **Pattern3** & 0 & 0 & 390 & 10 \\  & **Pattern4** & 2 & 2 & 6 & 390 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for the four fabric patterns used in the experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline
**SVM** & 80 & 0.3502 & 0.0516 \\
**ELM** & 84 & 0.0532 & 0.030 \\
**D-ELM** & 97.5 & 0.1803 & 0.035 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speed comparison of the proposed method with others. (Recognition accuracy is taken from quoted references [17]).  
complex textures efficiently. The proposed model was shown to yield higher accuracy for automated classification of woven fabric texture and yarn color, the recognition accuracy is also improved. Unlike other existing models, the proposed model is also capable of automatically recognizing the fabric and yarn color and classifying it to the respective color and texture class category. Overall, the relative success of the proposed biologically-inspired approach suggests that neuroscience may contribute new ideas and superior algorithms for automatic recognition and classification applications using color images.  
This work was supported by the National Natural Science Foundation of China (Nos. 11572084, 11472061, 71371046, 61603088), the Fundamental Research Funds for the Central Universities and DHU Distinguished Young Professor Program (No. 17D210402), and [PERSON] was supported by a grant from China Scholarship Council (CSC).  
[PERSON] and [PERSON] designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] wrote the paper; and [PERSON] and [PERSON] supervised the study and verified the findings of the study.  
The authors declare no conflict of interest.