Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers

Scene parsing consists in labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features. In parallel to feature extraction, a tree of segments is computed from a graph of pixel dissimilarities. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average “purity ” of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The system yields record accuracies on the the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) and near-record accuracy on the Stanford Background Dataset (8 classes), while being an order of magnitude faster than competing approaches, producing a 320 × 240 image labeling in less than 1 second, including feature extraction. 1. Overview Scene parsing, or full scene labeling (FSL), is the task of labeling each pixel in a scene with the category of the object to which it belongs. FSL requires to solve the detection, segmentation, recognition and contextual integration problems simultaneously, so as to produce a globally consistent labeling. One of the obstacles to FSL is that the information necessary for the labeling of a given pixel may