An End-to-End Framework for Clothing Collocation Based on Semantic Feature Fusion
IEEE MultiMedia(2020)
摘要
In this article, we develop an end-to-end clothing collocation learning framework based on a bidirectional long short-term memories (Bi-LTSM) model, and propose new feature extraction and fusion modules. The feature extraction module uses Inception V3 to extract low-level feature information and the segmentation branches of Mask Region Convolutional Neural Network (RCNN) to extract high-level semantic information; whereas the feature fusion module creates a new reference vector for each image to fuse the two types of image feature information. As a result, the feature can involve both low-level image and high-level semantic feature information, so that the performance of Bi-LSTM can be enhanced. Extensive simulations are conducted based on Ployvore and DeepFashion2 datasets. Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.
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关键词
semantic feature fusion,Bi-LTSM,feature extraction module,Inception V3,low-level feature information,segmentation branches,region convolutional neural network,feature fusion module,image feature information,high-level semantic feature information,Bi-LSTM,bidirectional long short-term memories model,end-to-end clothing collocation learning framework
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