Bark texture feature extraction based on statistical texture analysis

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.