Multicue MRF image segmentation: Combining texture and color features

Zoltan Kato, Ting Chuen Pong, Song Guo Qiang

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Herein, we propose a new Markov random field (MRF) image segmentation model which aims at combining color and texture features. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The uniqueness of our algorithm is that it provides both color only and texture only segmentations as well as a segmentation based on combined color and texture features. The number of classes on feature layers is given by the user but it is estimated on the combined layer.

Original languageEnglish
Pages (from-to)660-663
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume16
Issue number1
Publication statusPublished - Dec 1 2002

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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