Bark classification by combining grayscale and binary texture features

J. Song, Zheru Chi, J. Liu, H. Fu

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


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 publication[Missing Source Name from PIRA]
Number of pages4
Publication statusPublished - 2004
EventInternational Symposium on Intelligent Multimedia, Video and Speech Processing [ISIMP] -
Duration: 1 Jan 2004 → …


ConferenceInternational Symposium on Intelligent Multimedia, Video and Speech Processing [ISIMP]
Period1/01/04 → …


  • Edge detection
  • Feature extraction
  • Image classification
  • Image texture
  • Wavelet transforms

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