Feature Learning Based Multi-scale Wavelet Analysis for Textural Image Segmentation
In order to increase the edge accuracy and the areas consistency, and to reduce the partition error rate in textural image segmentation, we propose a new 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. This method includes three stages as follows: 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.
KeywordsTexture Image Segmentation Wavelet Transform Feature Learning
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