Evaluation of Texture Segmentation Algorithms

Kyong I. Chang, Kevin W. Bowyer, Munish Sivagurunath

CVPR 1999 pp. 1294-

doi:10.1109/CVPR.1999.786954 /cvpr/1999/chang1999cvpr-evaluation/

Abstract

This paper presents a method of evaluating unsupervised texture segmentation algorithms. The control scheme of texture segmentation has been conceptualized as two modular processes: (1) feature computation and (2) segmentation of homogeneous regions based on the feature values. Three feature extraction methods are considered: gray level co-occurrence matrix, Laws' texture energy and Gabor multi-channel filtering. Three segmentation algorithms are considered: fuzzy c-means clustering, square-error clustering and split-and-merge. A set of 35 real scene images with manually-specified ground truth was compiled. Performance is measured against ground truth on real images using region-based and pixel-based performance metrics.

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Cite

Text

Chang et al. "Evaluation of Texture Segmentation Algorithms." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.786954

Markdown

[Chang et al. "Evaluation of Texture Segmentation Algorithms." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/chang1999cvpr-evaluation/) doi:10.1109/CVPR.1999.786954

BibTeX

@inproceedings{chang1999cvpr-evaluation,
  title     = {{Evaluation of Texture Segmentation Algorithms}},
  author    = {Chang, Kyong I. and Bowyer, Kevin W. and Sivagurunath, Munish},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {1294-},
  doi       = {10.1109/CVPR.1999.786954},
  url       = {https://mlanthology.org/cvpr/1999/chang1999cvpr-evaluation/}
}