Texture classification by multi-model feature integration using Bayesian networks

Yong Huang

doi:10.1016/S0167-8655(02)00263-5

2003-01-01

PRE

DOI：10.1016/S0167-8655(02)00263-5

原文链接

原文求助

摘要

En 中文

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. (C) 2002 Elsevier Science B.V. All rights reserved.

Keyword:

Gaussian Markov random field

Gabor filter

feature integration

Bayesian networks

texture classification

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Pattern Recognition Letters

IF:

3.3

论文数:

7.9K

被引数:

1.6W

机构

暂无机构信息

引用论文

Multigap Superconductivity in RbCa₂Fe₄As₄F₂ Investigated Using μSR Measurements

Journal of the Physical Society of Japan

IF0

2018-12-15

OAAI

Devashibhai T. Adroja; Franziska K. K. Kirschner; Franz Lang; Michael Smidman; Adrian D. Hillier; Zhi-Cheng Wang; Guang-Han Cao; Gavin B. G. Stenning; Stephen J. Blundell

Bayesian network classifiers 贝叶斯网络分类器

MACHINE LEARNING

IF2.9

1997-01-01

3.8K

OAAI

Friedman, N; Geiger, D; Goldszmidt, M

Hybrid consensus theoretic classification

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

IF8.6

1997-07-01