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In this paper, we propose an end to end solution for image matting i.ehigh-precision extraction of foreground objects from natural images. Imagematting and background detection can be achieved easily through chroma keyingin a studio setting when the background is either pure green or blue.Nonetheless, image matting in natural scenes with complex and uneven depthbackgrounds remains a tedious task that requires human intervention. To achievecomplete automatic foreground extraction in natural scenes, we propose a methodthat assimilates semantic segmentation and deep image matting processes into asingle network to generate detailed semantic mattes for image composition task.The contribution of our proposed method is two-fold, firstly it can beinterpreted as a fully automated semantic image matting method and secondly asa refinement of existing semantic segmentation models. We propose a novel modelarchitecture as a combination of segmentation and matting that unifies thefunction of upsampling and downsampling operators with the notion of attention.As shown in our work, attention guided downsampling and upsampling can extracthigh-quality boundary details, unlike other normal downsampling and upsamplingtechniques. For achieving the same, we utilized an attention guidedencoder-decoder framework which does unsupervised learning for generating anattention map adaptively from the data to serve and direct the upsampling anddownsampling operators. We also construct a fashion e-commerce focused datasetwith high-quality alpha mattes to facilitate the training and evaluation forimage matting.

AlphaNet: An Attention Guided Deep Network for Automatic Image Matting