Best Feature Selection for Texture Classification

被引：0

作者：

Kim, Daeyoun ^{[1
]}

Liu, J. Jay ^{[2
]}

Han, Chonghun ^{[1
]}

机构：

[1] Seoul Natl Univ, Sch Chem & Biol Engn, San 56-1, Seoul 151742, South Korea

[2] Samsung Elect, Asan 336840, South Korea

来源：

10TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING | 2009年 / 27卷

关键词：

Wavelet texture analysis; texture classification; feature selection; product surfaces; best features selection; TRANSFORM;

D O I：

暂无

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Texture analysis techniques enable to determine the quality of product surfaces measured by image sensors. In previous works, wavelet texture analysis based on the conventional wavelet transform and wavelet packets have been recognized as the most successful technique for classifying steel quality. In this work, we propose a texture classification strategy based on a best feature selection method, which improves classification accuracy. Our proposed methodology has been applied and validated in the classification of the surface quality of rolled steel sheets.

引用

页码：1491 / 1496

页数：6

共 50 条

[21] Texture classification using feature selection and kernel-based techniques
Fernandez-Lozano, Carlos
Seoane, Jose A.
Gestal, Marcos
Gaunt, Tom R.
Dorado, Julian
Campbell, Colin
SOFT COMPUTING, 2015, 19 (09) : 2469 - 2480
[22] Clustering stability-based feature selection for unsupervised texture classification
Klepaczko, Artur
Materka, Andrzej
Machine Graphics and Vision, 2009, 18 (02): : 125 - 141
[23] A novel information theoretic approach to wavelet feature selection for texture classification
Naseem, Imran
Pham, Duc-Son
Venkatesh, Svetha
COMPUTERS & ELECTRICAL ENGINEERING, 2013, 39 (02) : 319 - 325
[24] Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification
Gemeay, Entessar Saeed
Alenizi, Farhan A.
Mohammed, Adil Hussein
Shakoor, Mohammad Hossein
Boostani, Reza
IEEE ACCESS, 2023, 11 : 91673 - 91695
[25] A Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification
Chi, Jianning
Yu, Xiaosheng
Zhang, Yifei
Wang, Huan
MATHEMATICAL PROBLEMS IN ENGINEERING, 2019, 2019
[26] Efficient Text Classification Using Best Feature Selection and Combination of Methods
Srinivas, M.
Supreethi, K. P.
Prasad, E. V.
Kumari, S. Anitha
HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: DESIGNING INFORMATION ENVIRONMENTS, PT I, 2009, 5617 : 437 - +
[27] Texture feature coding method for texture classification
Horng, MH
OPTICAL ENGINEERING, 2003, 42 (01) : 228 - 238
[28] Texture Feature Selection Using GA for Classification of Human Brain MRI Scans
Tajik, M. Nouman
Rehman, Atiq ur
Khan, Waleed
Khan, Baber
ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT II, 2016, 9713 : 233 - 244
[29] Improved Classification of Medical Data Using Meta-Best Feature Selection
Chaplin, Matthew
Grubb, Jacob
Clifford, Thomas
Bruce, Justin
Obafemi-Ajayi, Tayo
Matta, John
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, 2020, : 5602 - 5605
[30] Feature selection for classification
Department of Information Systems and Computer Science, National University of Singapore, Singapore 119260, Singapore
Intell. Data Anal., 3 (131-156):

← 1 2 3 4 5 →