Title: Segmentation of Instances by Hashing,
Abstract: We propose a novel approach to address the Simultaneous Detection and
Segmentation problem. Using hierarchical structures 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 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. We conduct extensive experiments on PASCAL VOC
2012 segmentation dataset, showing that CZ gives competitive state-of-the-art
object segmentations.