Bark classification by combining grayscale and binary texture features

Jiatao SONG, Zheru CHI, Jilin LIU, Hong FU

Research output: Chapter in Book/Report/Conference proceedingChapters

8 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. Copyright © 2004 by the Institute of Electrical and Electronics Engineers, Inc.
Original languageEnglish
Title of host publicationProceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech processing, ISIMP 2004
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages450-453
ISBN (Print)0780386876
DOIs
Publication statusPublished - 2004

Citation

Song, J., Chi, Z., Liu, J., & Fu, H. (2004). Bark classification by combining grayscale and binary texture features. In Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech processing, ISIMP 2004 (pp. 723-726). Piscataway, NJ: IEEE.

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