Abstract: Clothing classification based on deep learning has developed rapidly with the release of large-scale fashion data sets.However,most of the current clothing image classification methods are performed in a scene where the same clothing has a single,frontal or close-to-front image,which leads to misclassification of clothing when the view of clothing changes.In reality,the clothing features such as large deformation and severe occlusion further aggravate the problem.Therefore,a clothing image set classification method based on manifold structure neural network is proposed which uses manifold space to better represent the internal structure characteristics of clothing.Concretely,first,the shallow features of the clothing image set are extracted through the traditional convolutional neural network,and then the Euclidean feature data are converted into manifold data by using the covariance pooling.Finally,the internal manifold structures of clothing image sets are learned through the neural network based on manifold structure to obtain more accurate classification results.The experimental results show that the Precision,Recall and F-1 score of the proposed method on the MVC dataset can reach 89.64%,89.12% and 88.69%.Compared with the existing image sets (video) classification algorithms,the proposed method obtains an improvement of 2.04%,2.65% and 2.70%.It is illustrated that the proposed method is more accurate,efficient and robust than existing methods.

Key words: Clothing image set classification, Computer vision, Deep learning, Fashion analysis, Manifold neural network