Gaussian Derivative Models and Ensemble Extreme Learning Machine for Texture Image Classification

Abstract

In This Paper, We Propose an Innovative Classification Method Which Combines Texture Features of Images Filtered by Gaussian Derivative Models with Extreme Learning Machine (Elm). in the Texture Image Classification, Feature Extraction is a Very Crucial Step. Thusly, We Use Linear Filters Consisting of Two Gaussian Derivative Models, Difference of Gaussian (Dog) and Difference of Offset Gaussian (Doog), to Detect Texture Information of Images. Besides, Ensemble Extreme Learning Machine (E2lm) is Proposed to Reduce the Randomness of Original Elm and Used as the Classifier in This Paper. We Evaluate the Performance of Both the Texture Features and the Classifier E2lm by using Three Datasets: Brodatz Album, Vistex Database and Berkeley Image Segmentation Database. Experimental Results Indicate that Gaussian Derivative Models Are Superior to Gabor Filters, and E2lm Outperforms the Support Vector Machine (Svm) and Elm in Classification Accuracy.

Department(s)

Engineering Management and Systems Engineering

Comments

National Natural Science Foundation of China, Grant L2015B19

Keywords and Phrases

Ensemble extreme learning machine; Extreme learning machine; Gabor filters; Gaussian derivative models; Texture classification

International Standard Serial Number (ISSN)

1872-8286; 0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

14 Feb 2018

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