Non-parametric similarity measures for unsupervised texture segmentation and image retrieval 论文
2002引用 248
Image Retrieval and Classification TechniquesAdvanced Image and Video Retrieval TechniquesMedical Image Segmentation Techniques
详细信息
- 发表日期
- 2002-11-22
- 发表年份
- 2002
关键词
Image Retrieval and Classification TechniquesAdvanced Image and Video Retrieval TechniquesMedical Image Segmentation Techniques
摘要
In this paper we propose and examine non-parametric statistical tests to define similarity and homogeneity measures for textures. The statistical tests are applied to the coefficients of images filtered by a multi-scale Gabor filter bank. We demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer a unified approach to these closely related tasks. We present results on Brodatz-like micro-textures and a collection of real-word images.