Abstract
In this paper, a mechanism for unsupervised texture segmentation is presented. The approach is based on the multiscale representation of the discrete (dyadic) wavelet transform which can be implemented by a fast iterative algorithm. For unsupervised segmentation it is generally difficult to determine the number of classes to be identified. The proposed approach offers an approach to circumvent this problem. Our method utilizes a set of high-frequency channel energies to characterize texture features, followed by a multi-thresholding technique for coarse segmentation. The coarsely segmented results at the same scale are incorporated by an intra-scale fusion procedure. A fine segmentation technique is then used to reclassify the ambiguously labeled pixels generated from the intra-scale fusion step. Finally, the number of texture classes is determined by an inter-scale fusion in which the segmentation results at multiple scales are integrated. The performance of this method is demonstrated by several experiments on synthetic images, natural textures from Brodatz's album and real-world textured images. Since the choice of wavelets is very extensive and open, we further explore various types of wavelets for texture segmentation. The time cost of the proposed method is also measured.
| Original language | English |
|---|---|
| Pages (from-to) | 729-742 |
| Number of pages | 14 |
| Journal | Pattern Recognition |
| Volume | 30 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1997 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
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Unsupervised texture segmentation via wavelet transform. / Lu, Chun Shien; Chung, Pau-Choo; Chen, Chih Fan.
In: Pattern Recognition, Vol. 30, No. 5, 01.01.1997, p. 729-742.Research output: Contribution to journal › Article
TY - JOUR
T1 - Unsupervised texture segmentation via wavelet transform
AU - Lu, Chun Shien
AU - Chung, Pau-Choo
AU - Chen, Chih Fan
PY - 1997/1/1
Y1 - 1997/1/1
N2 - In this paper, a mechanism for unsupervised texture segmentation is presented. The approach is based on the multiscale representation of the discrete (dyadic) wavelet transform which can be implemented by a fast iterative algorithm. For unsupervised segmentation it is generally difficult to determine the number of classes to be identified. The proposed approach offers an approach to circumvent this problem. Our method utilizes a set of high-frequency channel energies to characterize texture features, followed by a multi-thresholding technique for coarse segmentation. The coarsely segmented results at the same scale are incorporated by an intra-scale fusion procedure. A fine segmentation technique is then used to reclassify the ambiguously labeled pixels generated from the intra-scale fusion step. Finally, the number of texture classes is determined by an inter-scale fusion in which the segmentation results at multiple scales are integrated. The performance of this method is demonstrated by several experiments on synthetic images, natural textures from Brodatz's album and real-world textured images. Since the choice of wavelets is very extensive and open, we further explore various types of wavelets for texture segmentation. The time cost of the proposed method is also measured.
AB - In this paper, a mechanism for unsupervised texture segmentation is presented. The approach is based on the multiscale representation of the discrete (dyadic) wavelet transform which can be implemented by a fast iterative algorithm. For unsupervised segmentation it is generally difficult to determine the number of classes to be identified. The proposed approach offers an approach to circumvent this problem. Our method utilizes a set of high-frequency channel energies to characterize texture features, followed by a multi-thresholding technique for coarse segmentation. The coarsely segmented results at the same scale are incorporated by an intra-scale fusion procedure. A fine segmentation technique is then used to reclassify the ambiguously labeled pixels generated from the intra-scale fusion step. Finally, the number of texture classes is determined by an inter-scale fusion in which the segmentation results at multiple scales are integrated. The performance of this method is demonstrated by several experiments on synthetic images, natural textures from Brodatz's album and real-world textured images. Since the choice of wavelets is very extensive and open, we further explore various types of wavelets for texture segmentation. The time cost of the proposed method is also measured.
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UR - http://www.scopus.com/inward/citedby.url?scp=0031143685&partnerID=8YFLogxK
U2 - 10.1016/S0031-3203(96)00116-1
DO - 10.1016/S0031-3203(96)00116-1
M3 - Article
AN - SCOPUS:0031143685
VL - 30
SP - 729
EP - 742
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
IS - 5
ER -