Automatic object extraction from images using deep neural networks and the level‐set method

The authors propose an automatic method for extracting objects with fine quality from photographs. The authors’ method starts with finding bounding boxes that enclose potential objects, which is achievable by state‐of‐the‐art object proposal methods. To further segment objects within obtained bounding boxes, the authors propose a new multi‐pass level‐set method based on saliency detection and foreground pixel classification. The level‐set function is initially constructed with respect to the automatically detected salient parts within the bounding box, which eliminates potential user interaction and predicts an initial set of pixels on the object. The input features for foreground pixel classifiers are constructed as a combination of classical texture features from the Gabor filter banks and convolutional features from a pre‐trained deep neural network. Through multi‐pass evolution of the level‐set function and re‐training of the foreground pixel classifier, the authors’ method is able to overcome possible inaccuracies in the initial level‐set function and converge to the real object boundary.