中文摘要：

随着人们对服装产品多元化需求的增强，无论是对于经营者还是消费者，服装分类都显得十分必要。现有方法大都基于服装整体做出处理，而忽略了服装细节要素的特征。提出针对服装细节，如衣领类型、袖子和下装长度等进行识别分类方法。在轮廓提取的基础上，针对衣领位置不确定、领口形状受周边花纹干扰等难点，设计了在多尺度HOG结果上进行投票的方法，并结合基于角点检测的几何特征提取如关键尺寸比例计算等，用SVM完成训练分类。最后利用多个特征搭配系数矩阵给出服装搭配建议。实验表明，该方法能够有效地完成上述服装细节要素分类，对自动搭配推荐也有一定实用价值。

英文摘要：

Due to the increased demand for the diversification of clothing products, clothes classification is very necessary, no matter for operators or consumers. Existing methods usually solve the problem based on the whole clothes, paying little attention to the detail features on the clothes. Therefore, identifying and classifying the detail features of clothes are emphasized in this paper such as the type of collar, the length of sleeves and the trousers or the dresses. Based on the contour extraction, the paper proposes the voting strategy for the results gained from multi-scale HOG features. Also the geometric features are used based on corner detection, solving problems like collar position uncertainty, neckline shape interference caused by surrounding patterns and so on. Then, SVM classifier is used to get the final results. After that, some advice is also provided on costume matching using multiple coefficient matrixes of features matching. Experiments show that our method is effective for classifying some mentioned detail features. Also, it shows some practical value for the automatic matching recommendation.