Textural image segmentation with multi-scale wavelet analysis based on feature learning
Yuelei Xu, Hongxiao Feng, Song Tian, Junwei Li · 2011
In order to improve the edge accuracy and the areas consistency, to reduce the partition error rate in textural image segmentation, we propose a new method which using multi-scale wavelet analysis based on feature learning in this paper. It improves the textural image segmentation by reducing the effect of redundant features on segmentation results. The method includes three stages as feature extraction, optimizing the feature vectors and feature space clustering. In the stage of filtrating valid features, we optimize the feature vectors by feature learning. The experimental results demonstrate that the improved algorithm is effective for textural image segmentation.