Research on Clothing Color Classification Method based on Improved FCM Clustering Algorithm

In the apparel industry, apparel color is an important factor to enhance the market competitiveness of enterprise products. However, the current prediction samples of clothing fashion color styling information do not incorporate practical cutting-edge fashion information. Therefore, Self-adaptive Weighted Kernel Function (SWK) has been introduced to traditional Fuzzy C-Means (FCM) clustering algorithms. After improvement, the SWK-FCM clustering algorithm is obtained, which enhances the classification ability of fashion colors and hue. Two prediction models have been developed using the finalized data of the International Fashion Color Committee, along with the SWK-FCM clustering algorithm. The models have been tested via experiments to verify their accuracy. The experimental results show that the classification coefficients of SWK-FCM clustering algorithm are 0.9553 and 0.9258 under 5% Gaussian noise. They are higher than those of FCM (0.7063) and FLICM (0.8598). The classification entropy is lower than that of the comparison algorithm, while the same results are presented under other conditions and in the actual experiments. In addition, the overall MSE of the GM (1, 1) prediction model using the final case information is 0.00028, which is close to the order of 10-4. The MSE value of the BP neural network prediction model using the final case information ranges from 0.000529 to 0.011025. Overall, the clustering algorithm of SWK-FCM has good classification performance. Additionally, the GM (1,1) model based on SWK-FCM has better prediction results, which can be effectively applied in practical clothing color classification and popular color prediction.

@article{Liu2023,
  title     = {Research on Clothing Color Classification Method based on Improved FCM Clustering Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jinliang Liu},
  doi       = {10.14569/IJACSA.2023.01409102},
  url       = {https://doi.org/10.14569/IJACSA.2023.01409102}
}