纹理图像分类的置信规则库推理方法

doi:10.3969/j.issn.0255-8297.2017.05.002

Abstract

Abstract:

To improve precision of traditional texture image classify algorithm, a new texture image classifcation method based on belief rule-base inference methodology using evidential reasoning approach(RIMER) is proposed. Researches on texture image classifcation generally consider improving texture feature extraction, and the design of classifer that is crucial to classifcation precision is largely ignored. In this paper, a rule-base inference method using an evidential reasoning approach is proposed. The classifer is redesigned based on the current methods of texture feature extraction. Algorithms of angular-radialtransform and gray-level con-occurrence matrix are used to extract texture image feature. Principle component analysis is carried out to solve the problem that the size of a belief rule base(BRB) classifer is controlled within a feasible range. The approach of rule-base inference method with evidential reasoning transforms the texture features into classifed belief degree information. Practicability and effectiveness of the proposed approach is validated in a case study.

Key words: belief rule base, texture image classifcation, gray-level con-occurrence matrix (GLCM), evidential reasoning, angular radial transform (ART)

CLC Number:

TP18

FANG Zhi-jian, FU Yang-geng, CHEN Jian-hua. Belief Rule Base Inference for Texture Image Classifcation[J]. Journal of Applied Sciences, 2017, 35(5): 545-558.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2017.05.002

https://www.jas.shu.edu.cn/EN/Y2017/V35/I5/545

References

[1] Haralick R M, Shabmugam K, Dinstein I H. Textural features for image classifcation[J]. IEEE Transactions on Systems, Man and Cybernetics, 1973, 3(6):610-621.
[2] Bober M. MPEG-7 visual shape descriptors[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6):716-719.
[3] Manjunath B S, MA W Y. Texture features for browsing and retrieval of image data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(8):837-842.
[4] Li W, Canini M, Moore A W, Bolla R. Efcient application identifcation and the temporal and spatial stability of classifcation schema[J]. Computer Networks, 2009, 53(6):790-809.
[5] Hwang S K, Kim W Y. Fast and efcient method for computing ART[J]. IEEE Transactions on Image Processing, 2006, 15(1):112-117.
[6] Kotoulas L, Andreadis I. An efcient technique for the computation of ART[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(5):682-686.
[7] Wahdan O M, Omar K, Nasrudin M F. Logo recognition system using angular radial transform descriptors[J]. Journal of Computer Science, 2011, 7(9):1416-1422.
[8] Chan T H, Jia K, Gao S H, Lu J W, Zeng Z N, Ma Y. A simple deep learning baseline for image classifcation?[J]. IEEE Transactions on Image Processing, 2015, 24(12):5017-5032.
[9] HU J, LI D, Duan Q, Han Y Q, Chen G F, Si X L. Fish species classifcation by color texture and multi-class support vector machine using computer vision[J]. Computers and Electronics in Agriculture, 2012, 88(1):133-140.
[10] 王刚. 基于角度径向变换的旋转不变纹理分类[J]. 计算机与现代化,2015(6):46-50. Wang G. Rotation-invariant texture classifcations based on angular radial transform[J]. Computer and Modernization, 2015(6):46-50. (in Chinese)
[11] 李保俊,蔡述庭,陈平. 基于角半径变换的五金器件识别系统的研究[J]. 计算机应用与软件,2015, 32(10):181-183. Li B J, Cai S T, Chen P. Research on hardware articles recognition system based on angular radial transform[J]. Computer Applications and Software, 2015, 32(10):181-183. (in Chinese)
[12] 张怡卓,许超,李想,薛瑞. 基于灰度共生矩阵的板材纹理模糊分类器设计[J]. 东北林业大学学报,2014, 42(4):127-130. Zhang Y Z, Xu C, Li X, Xue R. Design of fuzzy classifer for wood board texture based on GLCM[J]. Journal of Northeast Forestry University, 2014, 42(4):127-130. (in Chinese)
[13] 陈英,杨丰玉,符祥. 基于支持向量机和灰度共生矩阵的纹理图像分割方法[J]. 传感器与微系统,2012, 31(9):60-63. Chen Y, Yang F Y, Fu X. Detection changes of topographic map database using space overlay analysis[J]. Transducer and Microsystem Technologies, 2012, 31(9):60-63. (in Chinese)
[14] Yang J B, Liu J, Wang J, Sii H S. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans, 2006, 36(2):266-285.
[15] Sun R. Robust reasoning:integrating rule-based and similarity-based reasoning[J]. Artifcial Intelligence, 1995, 75(2):241-295.
[16] DempsterA P. A generalization of Bayesian inference[J]. Journal of the Royal Statistical Society, 1968, 30(2):205-247.
[17] Shafer G. A mathematical theory of evidence[M]. Princeton:Princeton University Press, 1976.
[18] Hwang C L, Yoon K. Methods for multiple attribute decision making[M]//Multiple Attribute Decision Making. Berlin Heidelberg:Springer, 1981:58-191.
[19] Zadeh L A. Fuzzy sets[J]. Information and Control, 1965, 8(3):338-353.
[20] 周志杰,杨剑波,胡昌华. 置信规则库专家系统与复杂系统建模[M]. 北京:科学出版社,2011.
[21] Liu J, Yang J B, Ruan D, Maetinez L, Wang J. Self-tuning of fuzzy belief rule bases for engineering system safety analysis[J]. Annals of Operations Research, 2008, 163(1):143-168.
[22] Jiang J, Li X, Zhou Z, Xu D L, Chen Y W. Weapon system capability assessment under uncertainty based on the evidential reasoning approach[J]. Expert Systems with Applications, 2011, 38(11):13773-13784.
[23] Zhou Z G, Liu F, Jiao L C, Zhou Z J, Yang J B, Gong M G, Zhang X P. A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer[J]. Knowledge-Based Systems, 2013, 54(1):128-136.
[24] Calzada A, Liu J, Wang H, Kashyap A. A new dynamic rule activation method for extended belief rule-based systems[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(4):880-894.
[25] Zhou Z G, Liu F, Li L L, Jiao L C, Zhou Z J, Yang J B, Wang Z L. A cooperative belief rule based decision support system for lymph node metastasis diagnosis in gastric cancer[J]. Knowledge-Based Systems, 2015, 85(1):62-70.
[26] USC University of Southern California. Signal and image processing institute[DB/OL]. 2017-02-10[2017-03-09]. http://sipi.usc.edu/database/database.php?volume=textures.

Belief Rule Base Inference for Texture Image Classifcation

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	WU Wenqiang, CHEN Aibin, LI Xiaoyao. Forest Image Dehazing Based on Feature Fusion Attention and Contrastive Learning [J]. Journal of Applied Sciences, 2026, 44(1): 97-109.
[2]	LIN Jiaan, TANG Xiaoyong. Multi-source Heterogeneous Data Missing Value Filling Method for Multi-process Lithium Battery Manufacturing Process [J]. Journal of Applied Sciences, 2025, 43(5): 785-798.
[3]	WANG Jingwei, WANG Haihua, WU Hao, LUO Xiangyang, MA Bin. Improvement of Adversarial Transferability via Transferability Gap [J]. Journal of Applied Sciences, 2025, 43(5): 799-807.
[4]	LUO Hongmei, WANG Changjiang, YANG Peijie, GUAN Xiaoyan, ZHOU Xiaojie, YU Hang. Seismic Phase Clustering Analysis Technology Based on Multi-granularity Ensemble Learning [J]. Journal of Applied Sciences, 2025, 43(4): 643-655.
[5]	LIU Hui, ZHANG Zhi, CHEN Yupeng. Entity Relationship Extraction Method Based on Bidirectional Decoding [J]. Journal of Applied Sciences, 2025, 43(3): 491-503.
[6]	KOU Yawen, ZHOU Yangming, WANG Zhe. Hybrid Memetic Algorithm for Soft-Clustered Capacitated Arc Routing Problem [J]. Journal of Applied Sciences, 2025, 43(2): 274-287.
[7]	LIAN Xiaofeng, KANG Maomao, TAN Li, WANG Yanli. A Semantic Segmentation Network Based on Lightweight Convolutional Modules [J]. Journal of Applied Sciences, 2025, 43(1): 66-79.
[8]	HU Wangxin, CHENG Yingchao, HE Yulin, HUANG Zhexue, CAI Zhanchuan. Benchmarking of Spiking Neural Networks and Performance Evaluation of Neuromorphic Training Frameworks [J]. Journal of Applied Sciences, 2025, 43(1): 169-182.
[9]	JIAO Guie, WENG Tongtong, ZHANG Wenjun. Unbalanced Multiclassification Study Based on Mixed Sampling and SE_ResNet_SVM [J]. Journal of Applied Sciences, 2024, 42(6): 1000-1015.
[10]	TANG Yiheng, WANG Yongxiong, WANG Zhe, ZHANG Xiaoli. Emotion Recognition of EEG Using Subdomain Adaptation and Spatial-Temporal Learning [J]. Journal of Applied Sciences, 2024, 42(6): 1016-1026.
[11]	JIN Yanliang, FAN Baorong, GAO Yuan, WANG Xiaoyong, GU Chenjie. Autonomous Driving Algorithm Based on Meta-Learning and Reinforcement Learning [J]. Journal of Applied Sciences, 2024, 42(5): 795-809.
[12]	MA Feihu, LEI Haoan, SUN Cuiyu, LUO Jiajie. Highway Toll Evasion Patterns Identification Based on RFE-OPTUNA-XGBoost Model [J]. Journal of Applied Sciences, 2024, 42(5): 857-870.
[13]	TANG Hong, DENG Feng, ZHANG Kai, NIE Xuefang, LI Guanghui. Lane Line Detection Based on CNN and Transformer Hybrid Network [J]. Journal of Applied Sciences, 2024, 42(5): 871-883.
[14]	LI Fei, SU Zhaopin, WANG Niansong, YANG Bo, ZHANG Guofu. Intelligent Synthetic Voice Speaker Verification Method Based on Group-Res2Block [J]. Journal of Applied Sciences, 2024, 42(4): 709-722.
[15]	XU Zengmin, LU Guangjian, CHEN Junyan, CHEN Jinlong, DING Yong. Person Re-identification Algorithm Based on Channel Feature Aggregation [J]. Journal of Applied Sciences, 2023, 41(1): 107-120.