Supervised Texture Segmentation: A Comparative Study
This is an incremental study for researchers in computer vision, providing a comparative analysis of existing methods on texture segmentation.
The paper compared four feature extraction methods for texture segmentation, finding that Gabor filters performed best in segmentation quality and co-occurrence matrices localized texture boundaries better.
This paper aims to compare between four different types of feature extraction approaches in terms of texture segmentation. The feature extraction methods that were used for segmentation are Gabor filters (GF), Gaussian Markov random fields (GMRF), run-length matrix (RLM) and co-occurrence matrix (GLCM). It was shown that the GF performed best in terms of quality of segmentation while the GLCM localises the texture boundaries better as compared to the other methods.