Unsupervised texture segmentation by determining the interior of texture regions based on wavelet transform
Traditional approaches for texture segmentation via wavelet transform usually adopt textural features to achieve segmentation purposes. However, for a natural image, the characteristics of the pixels in a texture region are not similar everywhere from a global viewpoint, and over-segmentation often occurs. To deal with this issue, an unsupervised texture segmentation method based on determining the interior of texture regions is proposed. The key idea of the proposed method is that if the pixels of the input image can be classified into interior pixels (pixels within a texture region) and boundary ones, then the segmentation can be achieved by applying region growing on the interior pixels and reclassifying boundary pixels. Based on the fact that each pixel P within a texture region will have similar characteristics with its neighbors, after applying wavelet transform, pixel P will have similar response with its neighbors in each transformed subimage. Thus, by applying a multilevel thresholding technique to segment each subimage into several regions, pixel P and its neighbors will be assigned to the same region in most subimages. Based on these segmented results, an interior pixels finding algorithm is then provided to find all interior pixels of textural regions. The algorithm considers a pixel which is in the same region as its neighbors in most subimages as an interior pixel. The effectiveness of this method is proved by successfully segmenting natural texture images and comparing with other methods.
texture segmentation wavelet transform multiresolution segmentation unsupervised clustering