基于复杂网络提取和支持向量机模型分类的服装领型研究

Research on clothing collar types based on complex network extraction and support vector machine classification

为解决服装打版中款式自动搜索的问题,以服装衣领款式的结构特征为背景,以服装圆领型图像为例,先通过构建复杂网络对其进行复杂网络特征的描述与提取,然后采用支持向量机的模型实现8种衣领类型图像的分类。实验结果表明:样本整体的平均分类准确率为98%,各类别的平均分类准确率均达到96%以上,其中,圆领的平均分类准确率为100%;在原样本图像库中加入一定程度椒盐噪声和高斯噪声后,样本整体的分类准确率在80%上下浮动,表明支持向量机模型分类的方法适用于含有一定程度噪声的图像识别。因而,本文基于复杂网络提取和支持向量机模型分类的服装领型研究的提取和分类准确率高,且分类结果相对稳定。

In order to achieve automatic style search in clothing pattern-making,this research took the structural features of clothing collar styles as working object,using clothing round-neck images as an example.The paper described and extracted complex network features by constructing a complex network,and the support vector machine model was used to classify images of 8 types of collars.The experimental results show that the average classification accuracy of the samples as a whole is 98%,and the average classification accuracy of each category is above 96%.Among them,the average classification accuracy rate for the round collar samples is 100%.At the same time,in order to evaluate the anti-noise performance of the feature extraction algorithm,after adding a certain degree of salt and pepper noise and Gaussian noise to the image of the original sample library,the overall classification accuracy of the sample fluctuates around 80%,indicating that the support vector machine classification method is suitable for image recognition with a certain degree of noise.To conclude,the extraction and classification accuracy of clothing collar research based on complex network extraction and support vector machine classification is high,and the classification results are relatively stable.