Segmentation of Instances by Hashing

We propose a novel approach to address the problem of Simultaneous Detection and Segmentation introduced in [Hariharan et al. 2014]. Using the hierarchical structures first presented in [Arbel\'aez et al. 2011] we use an efficient and accurate procedure that exploits the hierarchy feature information using Locality Sensitive Hashing. We build on recent work that utilizes convolutional neural networks to detect bounding boxes in an image [Ren et al. 2015] and then use the top similar hierarchical region that best fits each bounding box after hashing, we call this approach CZ Segmentation. We then refine our final segmentation results by automatic hierarchy pruning. CZ Segmentation introduces a train-free alternative to Hypercolumns [Hariharan et al. 2015]. We conduct extensive experiments on PASCAL VOC 2012 segmentation dataset, showing that CZ gives competitive state-of-the-art object segmentations.