Abstract: We propose an algorithm for classifying clothing image attributes. To handle the noise in clothing images, key parts of clothing are located by a well-trained human part detector, and redundant information is eliminated, by which means the accuracy of clothing attribute classification is improved. Additionally, a novel feature descriptor based on human skeleton and skin is also proposed. This descriptor describes clothing feature with fewer dimensions, which significantly speeds up classifiers of related attributes. To deal with the complex semantic of clothing attributes, different SVM Decision Tree models are built for different attributes, which improves the efficiency of classification and achieves the objective of both coarse-grained and fine-grained classification. Experiments demonstrate the effectiveness of the proposed algorithm on multiple clothing attribute classification tasks.