Automatic segmentation of clothing images based on Faster R-CNN and Grabcut algorithm
In order to realize the automatic segmentation of clothing images,an image segmentation method based on Faster R-CNN combined with GrabCut is proposed.Firstly,the basic framework of the Fast R-CNN is used to subdivide the to-be-detected tasks of street photographs into six categories: tops,skirts,bags,etc.Next,adjust the model full connection layer parameters based on the original basic framework,the foreground object box is obtained as the initial frame of the GrabCut segmentation algorithm.Then we use GrabCut algorithm to extract garment area.The method locates the clothing position from the picture of the complex background,removes the complex background,and realizes the segmentation of the clothing area.The experimental results show that the proposed method has good natural contour detection and extraction ability,and is suitable for the detection of local weak contour edges of images and the processing of large-scale clothing image segmentation.Beyond that,it can be used for selective automatic style category extraction in large batch processing of images.Moreover,it also improves the efficiency of the garment image segmentation process.
Clothing image segmentation Faster R-CNN GrabCut imageextraction
Si Yang Chong Chen Zeng-Bo Xu
Shanghai University of Engineering Science, Shanghai, 201620, China
国际会议
2019上海纺织服装创意创新研究生学术论坛暨第十三届纺织服装创新国际论坛
上海
英文
130-134
2019-10-23（万方平台首次上网日期，不代表论文的发表时间）