Automatic Image Matting with Attention Mechanism and Feature Fusion

In response to the current problem of the excessive workload of manual matting and the inability of automatic matting to distinguish between multiple instances,an automatic matting algorithm with attention mechanism and feature fusion is proposed.The algorithm consists of two parts,the pre-segmentation module and the Alpha matting module,which adopt different structures respectively.The presegmentation module uses transfer learning method to fine-tune a mask scoring R-CNN to implement instance segmentation of multi-instance natural images and obtain a binary segmentation of the foreground individuals.Based on this,the Alpha module first pre-processes the binary segmentation map into a generate trimap,which is then fed into the Alpha matting module network along with the original input image.By designing different decoding strategies and attention mechanisms for the Alpha matting module,the accurate recovery of input image details is achieved.In a follow-up comparison experiment of foreground vehicle Alpha estimation,which uses a homemade vehicle data set but without human interaction,this algorithm achieves a higher matting accuracy with 19.2%lower SAD and 26.3%lower MSE than the existing DIM algorithm.