Bark classification by combining grayscale and binary texture features

Jiatao Song, Zheru Chi, Jilin Liu, Hong Fu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

10 Citations (Scopus)

Abstract

In this paper, a texture feature based bark classification method is presented. Our method uses two types of texture features: the co-occurrence matrix feature and the Long Connection Length Emphasis (LCLE) feature which is extracted from the binary bark image. For the extraction of binary texture maps, an improved wavelet-based edge detection algorithm is proposed. It includes two binarization steps and a post-processing step. The paper also presents an approach to combine two feature sets. Experiments on 18 different tree species and in total 90 bark images show that a combination of these two feature sets can achieve a much higher bark classification rate than that when each feature set is utilized individually.
Original languageEnglish
Title of host publication2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Pages450-453
Number of pages4
Publication statusPublished - 1 Dec 2004
Event2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004 - Hong Kong, China, Hong Kong
Duration: 20 Oct 200422 Oct 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

ASJC Scopus subject areas

  • Engineering(all)

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