# Segmentation of Objects by Hashing

## TL;DR

This paper introduces C&Z Segmentation, a train-free, hierarchical hashing method that improves object segmentation by combining CNN-based detection with efficient region matching, achieving competitive results on PASCAL VOC 2012.

## Contribution

It presents a novel, train-free segmentation approach that leverages hierarchical structures and Locality Sensitive Hashing for improved object segmentation.

## Key findings

- Achieves competitive segmentation accuracy on PASCAL VOC 2012
- Provides an efficient, train-free alternative to Hypercolumns
- Demonstrates effective hierarchical region matching with hashing

## Abstract

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 feature information of the hierarchy 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 C&Z Segmentation. We then refine our final segmentation results by automatic hierarchical pruning. C&Z Segmentation introduces a train-free alternative to Hypercolumns [Hariharan et al 2015]. We conduct extensive experiments on PASCAL VOC 2012 segmentation dataset, showing that C&Z gives competitive state-of-the-art segmentations of objects.