# Revisiting Graph Construction for Fast Image Segmentation

## TL;DR

This paper introduces a new graph construction method for fast image segmentation that leverages local and global region relationships, leading to improved efficiency and competitive accuracy on standard benchmarks.

## Contribution

The authors propose a novel graph construction approach based on co-occurrence and saliency, along with an energy function for efficient graph partitioning in image segmentation.

## Key findings

- Achieves competitive segmentation accuracy on BSDS500, PASCAL VOC, and COCO datasets.
- Significantly improves computational efficiency over existing methods.
- Effective multi-class segmentation driven by eigenvector histogram representations.

## Abstract

In this paper, we propose a simple but effective method for fast image segmentation. We re-examine the locality-preserving character of spectral clustering by constructing a graph over image regions with both global and local connections. Our novel approach to build graph connections relies on two key observations: 1) local region pairs that co-occur frequently will have a high probability to reside on a common object; 2) spatially distant regions in a common object often exhibit similar visual saliency, which implies their neighborship in a manifold. We present a novel energy function to efficiently conduct graph partitioning. Based on multiple high quality partitions, we show that the generated eigenvector histogram based representation can automatically drive effective unary potentials for a hierarchical random field model to produce multi-class segmentation. Sufficient experiments, on the BSDS500 benchmark, large-scale PASCAL VOC and COCO datasets, demonstrate the competitive segmentation accuracy and significantly improved efficiency of our proposed method compared with other state of the arts.