An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image
Abstract
:1. Introduction
2. Related Work
2.1. Hyper-Spectral Images
2.2. Clustering Ensemble
3. Skin Feature Segmentation Scheme
3.1. Band Selection and Generation of Basic Clustering
3.2. Construction and Re-Labeling of a Patch
3.3. MSF for Classification
Step 1: Choose several discriminative basic clusters (see Section 3.2) as the input of the ensemble process.Step 2: The basic clustering is represented by non-overlap neighborhood patch with 2 × 2 pixels. Depict it as a vector and re-represent it by calculating the mean spectral characteristic . |
Step 3: Integrate selected basic clustering, to obtain the optional patches. Then, select and re-label for reliable patches as markers, from the optional patches. |
Step 4: Group the adjacent image patches into series of Minimum Spanning Forests with 3 × 3 blocks; and then assign the unlabeled patch to markers according to the SID similarity criterion. |
3.4. Comparison Algorithm
4. Experimental Results and Discussion
4.1. Band Selection Results
4.2. Skin Feature Segmentation of the PolyU Hyper-Spectral Face Database
4.3. Skin Feature Segmentation of the UWA Hyper-Spectral Face Database
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Block Size | Precision-Brow | Recall-Brow | F1-Score-Brow | Precision-Eye | Recall-Eye | F1-Score-Eye | Precision-Mouse | Recall-Mouse | F1-Mcore-Mouse |
---|---|---|---|---|---|---|---|---|---|
2 × 2 | 0.75 | 0.89 | 0.82 | 0.76 | 1.00 | 0.86 | 0.89 | 0.88 | 0.88 |
4 × 4 | 0.13 | 0.19 | 0.15 | 0.53 | 0.83 | 0.88 | 0.55 | 0.64 | 0.59 |
Method | Precision-Brow | Recall-Brow | Precision-Eye | Recall-Eye | Precision-Mouse | Recall-Mouse |
---|---|---|---|---|---|---|
Standard-FCM | 0.92 | 0.04 | 0.97 | 0.2.0 | 0 | 0 |
spatial-FCM | 0.62 | 0.17 | 0.85 | 0.35 | 0.82 | 0.05 |
FRFCM | 0.65 | 0.96 | 0.79 | 0.84 | 0.89 | 0.79 |
Our method | 0.67 | 0.67 | 0.96 | 0.91 | 0.93 | 0.80 |
15th band | 0.59 | 0.66 | 0.80 | 0.77 | 1.00 | 0.66 |
23rd band | 0.67 | 0.86 | 0.82 | 0.78 | 1.00 | 0.14 |
Method | Precision-Brow | Recall-Brow | Precision-Eye | Recall-Eye | Precision-Beard | Recall-Beard |
---|---|---|---|---|---|---|
spatial-FCM | 1.00 | 0.45 | 1.00 | 0.32 | 0 | 0 |
12th band | 0.77 | 1.00 | 0.95 | 0.87 | 0.94 | 0.62 |
Our-method | 0.93 | 0.88 | 0.76 | 0.82 | 0.89 | 0.72 |
Method | Precision-Brow | Recall-Brow | Precision-Eye | Recall-Eye | Precision-Beard | Recall-Beard |
---|---|---|---|---|---|---|
spatial-FCM | 0.85 | 0.86 | 0.93 | 0.84 | 1.00 | 0.05 |
12th band | 0.58 | 1 | 0. 64 | 0.75 | 1.00 | 0.84 |
Our-method | 0.83 | 0.88 | 1.00 | 0.88 | 1.00 | 0.84 |
Method | Precision-Brow | Recall-Brow | Precision-Eye | Recall-Eye | Precision-Beard | Recall-Beard |
---|---|---|---|---|---|---|
Spatial-FCM | 0 | 0 | 1.00 | 0.07 | 0 | 0 |
12th band | 0.83 | 0.96 | 1.00 | 0.70 | 0.83 | 0.27 |
Our-method | 0.85 | 0.93 | 0.83 | 0.71 | 1.00 | 0.77 |
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Zhao, Y.; Wu, M.; Zhang, L.; Wang, J.; Wei, D. An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image. Information 2018, 9, 261. https://doi.org/10.3390/info9100261
Zhao Y, Wu M, Zhang L, Wang J, Wei D. An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image. Information. 2018; 9(10):261. https://doi.org/10.3390/info9100261
Chicago/Turabian StyleZhao, Yuefeng, Mengmeng Wu, Liren Zhang, Jingjing Wang, and Dongmei Wei. 2018. "An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image" Information 9, no. 10: 261. https://doi.org/10.3390/info9100261
APA StyleZhao, Y., Wu, M., Zhang, L., Wang, J., & Wei, D. (2018). An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image. Information, 9(10), 261. https://doi.org/10.3390/info9100261