Linear colour segmentation revisited

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 define...

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Bibliographic Details
Published in:arXiv.org 2019-01
Main Authors: Smagina, Anna, Bozhkova, Valentina, Gladilin, Sergey, Nikolaev, Dmitry
Format: Article
Language:English
Subjects:
Algorithms
Color
Datasets
Ground truth
Image segmentation
Pixels
Source code
Weight
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1901.00534