Bark texture feature extraction based on statistical texture analysis

Yuan Yuan Wan*, Ji Xiang Du, De Shuang Huang, Zheru Chi, Yiu Ming CHEUNG, Xiao Feng Wang, Guo Jun Zhang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

30 Citations (Scopus)

Abstract

This paper quantitatively describes and discusses the usefulness of texture analysis methods for the recognition of bark Comparative studies of bark texture feature extraction are performed for the four texture analysis methods such as the gray level Run-Length method (RLM), Co-occurrence Matrices method (COMM) and Histogram method (HM) as well as Auto-Correlation method (ACM). Specifically, we use three classifiers of Nearest Neighbor (1-NN), k-Nearest Neighbor (k-NN) and Moving Median Centers (MMC) Hypersphere classifiers to verify the validity of the extracted bark texture features. To gain good result we added the color information that proved very efficient. Moreover, the experimental results also demonstrate that from the viewpoint of the recognition accuracy and computational complexify, the COMM method is superior to the other three methods.

Original languageEnglish
Title of host publication2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Pages482-485
Number of pages4
Publication statusPublished - 2004
Event2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004 - Hong Kong, China, Hong Kong
Duration: 20 Oct 200422 Oct 2004

Publication series

Name2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004

Conference

Conference2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Country/TerritoryHong Kong
CityHong Kong, China
Period20/10/0422/10/04

Scopus Subject Areas

  • Engineering(all)

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