Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Markov random fields for texture classification
Pattern Recognition Letters
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
FRAME: Filters, Random fields, and Minimax Entropy-- Towards a Unified Theory for Texture Modeling
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Pyramid-based texture analysis/synthesis
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Modeling spatial and temporal textures
Modeling spatial and temporal textures
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Texture Classification Using Wavelet Decomposition with Markov Random Field Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
An adaptive level-selecting wavelet transform for texture defect detection
Image and Vision Computing
Object-based and semantic image segmentation using MRF
EURASIP Journal on Applied Signal Processing
International Journal of Remote Sensing
Extraction of bridges over water from IKONOS panchromatic data
International Journal of Remote Sensing
A hybrid feature selection approach based on the Bayesian network classifier and rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
ESVC-based extraction and segmentation of texture features
Computers & Geosciences
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In this paper, a texture classification method based on multi-model feature integration by Bayesian networks is proposed. Considering that many image textures exhibit both structural and statistical properties, two feature sets based on two texture models--the Gabor model and the Gaussian Markov random field model are used to describe the image properties in both structure and statistics. A Bayesian network classifier is then used to combine these two sets of features along with their individual confidence measures for texture classification. Seventy eight Brodatz textures were used to evaluate the classification performance. The results show that the proposed method is better than that using a single set of features from either model for texture classification.