Deep GrabCut for Object Selection

Understand

Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest.

• However it is common that a rectangle input could be too large or too small.
• In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map.
• A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs.