Research on the Clothing Classification of the She Ethnic Group in Different Regions Based on FPA-CNN

Abstract

In order to achieve the effective computer recognition of the She ethnic clothing from different regions through the extraction of color features, this paper proposes a She ethnic clothing classification method based on the Flower Pollination Algorithm-optimized color feature fusion and Convolutional Neural Network (FPA-CNN). The method consists of three main steps: color feature fusion, FPA optimization, and CNN classification. In the first step, a color histogram and color moment features, which can represent regional differences in She ethnic clothing, are extracted. Subsequently, FPA is used to perform optimal weight fusion, obtaining an optimized ratio. Kernel principal component analysis is then applied to reduce the dimensionality of the fused features, and a CNN is constructed to classify the She ethnic clothing from different regions based on the reduced fused features. The results show that the FPA-CNN method can effectively classify the She ethnic clothing from different regions, achieving an average classification accuracy of 98.38%. Compared to SVM, BP, RNN, and RBF models, the proposed method improves the accuracy by 11.49%, 7.7%, 6.49%, and 3.92%, respectively. This research provides a reference and guidance for the effective recognition of clothing through the extraction of color features.

Protection of Guizhou Miao Batik Culture Based on Knowledge Graph and Deep Learning

“…Ding et al [42] proposed a nearest-neighbor method-based image classification model for She clothing, which integrates the texture features and spatial layout features of the clothing texture to improve the classification accuracy. In their follow-up research [43] ,…”

Protection of Guizhou Miao batik culture based on knowledge graph and deep learning

“…Ding et al [42] proposed a nearest-neighbor method-based image classification model for She clothing, which integrates the texture features and spatial layout features of the clothing texture to improve the classification accuracy. In their follow-up research [43], they introduced CNN and designed a color feature fusion strategy optimized by the flower pollination algorithm, making the classification model simultaneously consider multiple visual features such as color, texture, and space of clothing, achieving better classification results. Kong et al [44] focused on the pattern classification problem of Yao ethnic clothing and brocade, proposing a multi-target classification method based on Faster R-CNN.…”