NDLI: Textural image segmentation with multi-scale wavelet analysis based on feature learning

Content Provider	IEEE Xplore Digital Library
Author	Yuelei Xu Hongxiao Feng Song Tian Junwei Li
Copyright Year	2011
Description	Author affiliation: Institute 207 of CASIC Second Academy; Beijing, China (Junwei Li) \|\| Design and Research Institute, Shaanxi Huanghe Group Co., Ltd., Xi'an, China (Hongxiao Feng) \|\| Engineering Institute, the Air Force University of Engineering; Xi'an, China (Yuelei Xu; Song Tian)
Abstract	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.
Starting Page	302
Ending Page	305
File Size	514583
Page Count	4
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6099939
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Image segmentation Accuracy Error analysis Image edge detection feature extraction wavelet analysis textural image segmentation Feature extraction Vectors clustering redundant features
Content Type	Text
Resource Type	Article

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