Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37684
Title: Bark texture feature extraction based on statistical texture analysis
Authors: Wan, YY
Du, JX
Huang, DS
Chi, Z 
Cheung, YM
Wang, XF
Zhang, GJ
Keywords: Correlation methods
Feature extraction
Image classification
Image colour analysis
Image recognition
Image texture
Statistical analysis
Wood
Issue Date: 2004
Source: Proceedings of the International Symposium on Intelligent Multimedia, Video and Speech Processing (ISIMP’2004), Hong Kong, 20-22 Oct. 2004, p. 482-485 How to cite?
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 (l-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 results we added to color information that proved very efficient. Moreover, the experimental results also demonstrate that from the viewpoint of the recognition accuracy and computational complexity, the COMM method is superior to the other three methods.
URI: http://hdl.handle.net/10397/37684
ISBN: 0-7803-8687-6
DOI: 10.1109/ISIMP.2004.1434106
Appears in Collections:Conference Paper

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