# Linear colour segmentation revisited

## TL;DR

This paper revisits linear colour segmentation algorithms, introduces a novel region adjacency graph-based method with a projective transform for better shadow handling, and demonstrates improved results on a new benchmark dataset.

## Contribution

It proposes a new segmentation algorithm based on region adjacency graphs and a projective transform, with demonstrated qualitative improvements over existing methods.

## Key findings

- Qualitative advantages over other model-based algorithms
- Positive effect of each proposed modification
- Effective handling of shadows and highlights

## Abstract

In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard's DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://github.com/visillect/segmentation.