Bark classification by combining grayscale and binary texture features

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.