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Content Provider | IEEE Xplore Digital Library |
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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 | |
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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