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《Intelligent Computer and Applications》 2019-04
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Research on virtual fitting model recommendation technology based on weighted random forest algorithm

LI Tao;FENG Xiangyang;School of Computer Science and Technology,Donghua University;  
With the development of somatosensory interaction technology,Kinect-based virtual fitting system provides a newoption for customers to purchase clothing online. Kinect can accurately measure the human skeleton model and generate three-dimensional data of the human body to further recommend the clothing model for customers and use the visualization technology for virtual trial installation. However,different people's body types are different,and different manufacturers' clothing models are not standardized. The traditional data screening and matching methods are always difficult for the customer to choose the size of the clothes. Based on the three-dimensional data of human body acquired by Kinect,this project designs a weighted random forest method to predict and recommend suitable clothing models for customers. Combined with other machine learning models,this paper conducts a comparative experiment. The results showthat the random forest model is effective and accurate,and the highest accuracy(100%) is obtained on 3 000 test samples. The model has strong generalization ability and it is robust and stable,which could be widely applied in the virtual fitting model recommendation scenario.
【CateGory Index】: TP391.41;TP181
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