Fully Convolutional Network with Superpixel Parsing for Fashion Web Image Segmentation.

In this paper we introduce a new method for extracting deformable clothing items from still images by extending the output of a Fully Convolutional Neural Network (FCN) to infer context from local units (superpixels). To achieve this we optimize an energy function, that combines the large scale structure of the image with the local low-level visual descriptions of superpixels, over the space of all possible pixel labellings. To assess our method we compare it to the unmodified FCN network used as a baseline, as well as to the well-known Paper Doll and Co-parsing methods for fashion images.