Segmentation and Shape Extraction from Convolutional Neural Networks

We propose a novel method for creating high-resolution class activation maps from a given deep convolutional neural network which was trained for image classification. The resulting class activation maps not only provide information about the localization of the main objects and their instances in the image, but are also accurate enough to predict their shapes. Rather than pursuing a weakly supervised learning strategy, the proposed algorithm is a multi-scale extension of the classical class activation maps using a principal component analysis of the classification network feature maps, guided filtering, and a conditional random field. Nevertheless, the resulting shape information is competitive with state-of-the-art weakly supervised segmentation methods on datasets on which the latter have been trained, while being significantly better at generalizing to other datasets and unknown classes.