关键词: 服装款式识别, 傅里叶描述子, 支持向量机, Hu不变矩, 主成分分析, 极端学习机

Abstract:

In the current clothing style recognition field, clothing contour feature extraction technique was complicated, classification efficiency was low and adaptability was poor. In order to solve these problem and recognize the clothing styles, a novel approach was proposed. In this approach, the contours were extracted from the clothing images, which were taken from the newly created sample database. Then the contour features were described by Fourier descriptors(FD). Finally, the clothing styles were classified by multiclass support vector machines(SVM). The experimental results show that this novel approach can accurately extract the contours of clothing. The recognition effect of the Fourier descriptors is better than the Hu moment invariant and feature fusion (Hu moment invariant and Fourier descriptor). Principal component analysis of FD can’t improve the recognition accuracy, and the classification effect of SVM is better than ELM. This approach can achieve a recognition rate above 95%. In particular, contour features obvious style has a better recognition rate.

Key words: clothing style recognition, Fourier descriptor, support vector machine, Hu moment invariant, principal component analysis, extreme learning machine