Research And Application Of Clothing Retrieval Algorithm Based On Video

At present,clothing products are one of the most in demand and category products on our national e-commerce platform.Therefore,the sales volume of clothing products will directly affect the revenue of the e-commerce platform.At the same time,with the rapid development of industries such as webcasting and short videos,watching live broadcasts or short videos has become a part of people's life.Therefore,recommending corresponding clothing purchase links in live broadcasts or short videos will provide a new traffic portal for clothing product sales on e-commerce platforms.Therefore,how to construct a video-based clothing retrieval model to improve the accuracy of retrieval is the key to increasing the sales of clothing products.The usage scenarios of the video-based clothing retrieval model are as follows.The model needs to extract features of the video containing the clothing to be detected,and then retrieve the corresponding clothing pictures in the image database based on the extracted features.The main contributions of this article are as follows:(1)Establish an image-based clothing retrieval framework.The clothing retrieval framework includes two sub-models,namely the clothing foreground detection model and the clothing retrieval model.The clothing foreground detection model is responsible for detecting the area of the clothing in the picture.This paper considers two application scenarios of real-time detection and offline detection,and compares the different performances of the two target detection frameworks,YoLo V5 and Faster RCNN.The clothing retrieval model is used to extract the feature expression of the clothing area obtained from the foreground detection task.This paper uses EfficientNet as the feature extraction network and ArcFace as the loss of the classification network to construct the clothing detection model.The final clothing retrieval accuracy rate reached 87.2%.(2)Based on the existing clothing foreground detection model,this paper propose a video-based key frame and clothing foreground detection model.This model is constructed by adding a video key frame prediction branch to the YoLo V5 and Faster RCNN models.The video key frame is a picture suitable for clothing retrieval,that is,the clothing information in the picture is more comprehensive.Under the framework of YoLo V5,the non-key frame prediction accuracy of the model reached 67%,and within the framework of Faster RCNN,the prediction accuracy of the model reached 75%.(3)Within the existing image clothing retrieval framework,the image-based clothing retrieval model is used as the pre-training model for feature extraction,and considering that the video frame sequence contains more information than a single video frame,this paper introduces the attention mechanism to construct an attention module that is used to fuse the features of the video frame sequence,and finally a video clothing retrieval model based on the attention mechanism is obtained.By comparative analysis,it is found that the retrieval accuracy of the model in this paper is 88.9% higher than that of the image-based clothing retrieval model,and its retrieval effect is better than other baseline models.