Bayesian fusion of color and texture segmentations
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 956-962 vol.2
- https://doi.org/10.1109/iccv.1999.790351
Abstract
In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a maximum descriptiveness criterion, and allows us to choose any number of classes for the final grouping. This technique also allows us to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture).Keywords
This publication has 4 references indexed in Scilit:
- Color- and texture-based image segmentation using EM and its application to content-based image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Texture features for browsing and retrieval of image dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Neural Networks for Pattern RecognitionPublished by Oxford University Press (OUP) ,1995