Fashion garment image segmentation method based on depth learning

Abstract

The invention relates to a fashion garment image segmentation method based on depth learning. The fashion garment segmentation method based on depth learning comprises the following steps of the construction of a depth neural network garment model, the loss function design of reverse error propagation and a model training strategy, wherein the depth neural network garment model comprises a featureextraction module, a garment semantic information extraction module and a garment segmentation prediction module, the loss functions comprise a regression function of a key point position, a key visibility loss function, a cross entropy loss function of a garment prediction category with weights and a regression loss function of a garment position; and the model training strategy comprises a weight parameter initialization method, the data preprocessing, an optimization algorithm and a training step. The method has the advantages of being able to automatically segment and recognize the upperbody clothing, lower body clothing and whole body clothing collocation in complex images, and being conducive to the deep learning and network training for fashion clothing design.

Description

technical field [0001] The present invention relates to the technical field of fashion clothing, in particular, it is a fashion clothing image segmentation method based on deep learning. Background technique [0002] Image segmentation is the most basic operation in computer vision processing, and the subsequent processing of computer vision depends on the quality of the segmentation of the region of interest in the image. Most of the existing image segmentation techniques use traditional algorithms for processing, such as statistical image energy histogram, edge detection (gradient) cutting. Or add mathematical morphology processing to the image to improve the accuracy of segmentation, such as noise reduction such as dilation and corrosion. The accuracy and efficiency of traditional image segmentation algorithms are acceptable when processing images with a single scene and strong pixel continuity; however, when processing complex fashion images, especially in complex scene...

AI Technical Summary

Problems solved by technology

However, regarding a pre-processing in a fashion clothing analysis system, that is, identifying upper body clothing, lower body clothing, and full-body clothing matching from complex scenes, and then applying it to artificial intelligence fashion clothing analysis and processing in the later stage, by giving upper body clothing and lower body clothing The semantic information of the source image is input into the deep learning network specially designed for the field of fashion clothing for training, and a fashion clothing image segmentation method based on deep learning is currently used to automatically identify the upper body clothing, lower body clothing, and full-body clothing matching in the image. no relevant reports

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