In this paper, a Grey clustering method is applied to the evaluation research of sporting clothing style, the result shows that the methods proposed in the paper is feasible and effective.
Clothing plays an important role in humans’social life as it can enhance people’s personal quality,and it is a practical problem by answering the question“which item should be chosen to match current fashion items ...Clothing plays an important role in humans’social life as it can enhance people’s personal quality,and it is a practical problem by answering the question“which item should be chosen to match current fashion items in a set to form collocational and compatible outfits”.Motivated by this target an end-to-end clothing collocation learning framework is developed for handling the above task.In detail,the proposed framework firstly conducts feature extraction by fusing the features of deep layer from Inception-V3 and classification branch of mask regional convolutional neural network(Mask-RCNN),respectively,so that the low-level texture information and high-level semantic information can be both preserved.Then,the proposed framework treats the collocation outfits as a set of sequences and adopts bidirectional long short-term memory(Bi-LSTM)for the prediction.Extensive simulations are conducted based on DeepFashion2 datasets.Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.展开更多
文摘In this paper, a Grey clustering method is applied to the evaluation research of sporting clothing style, the result shows that the methods proposed in the paper is feasible and effective.
基金National Natural Science Foundation of China(No.61971121)。
文摘Clothing plays an important role in humans’social life as it can enhance people’s personal quality,and it is a practical problem by answering the question“which item should be chosen to match current fashion items in a set to form collocational and compatible outfits”.Motivated by this target an end-to-end clothing collocation learning framework is developed for handling the above task.In detail,the proposed framework firstly conducts feature extraction by fusing the features of deep layer from Inception-V3 and classification branch of mask regional convolutional neural network(Mask-RCNN),respectively,so that the low-level texture information and high-level semantic information can be both preserved.Then,the proposed framework treats the collocation outfits as a set of sequences and adopts bidirectional long short-term memory(Bi-LSTM)for the prediction.Extensive simulations are conducted based on DeepFashion2 datasets.Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.
