Convolution-Deconvolution Image Segmentation Model for Fusion Features and Decision

Chenxiao Feng; Xili Wang

doi:10.3788/LOP56.011008

Journals >Laser & Optoelectronics Progress >Volume 56 >Issue 1 >Page 011008 > Article

Laser & Optoelectronics Progress
Vol. 56, Issue 1, 011008 (2019)

Convolution-Deconvolution Image Segmentation Model for Fusion Features and Decision

Chenxiao Feng¹ and Xili Wang^1、2、*

Author Affiliations

¹ School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China

² Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, Shaanxi 710119, China

show less

DOI: 10.3788/LOP56.011008 Cite this Article Set citation alerts

Chenxiao Feng, Xili Wang. Convolution-Deconvolution Image Segmentation Model for Fusion Features and Decision[J]. Laser & Optoelectronics Progress, 2019, 56(1): 011008 Copy Citation Text

show less

Fig. 1. Convolution-deconvolution image segmentation model for fusion features and decision

Download full size

Fig. 2. Flow charts of data processing. (a) Data processing of CD-FFD; (b) each branch network data processing of CD-FFD

Download full size

Fig. 3. Segmentation results of CD-FFD model. (a) RGB image; (b) gray image; (c) segmentation result of RGB-Net; (d) segmentation result of GRAY-Net; (e) segmentation result of CD-FFD; (f) ground-truth

Download full size

Fig. 4. Segmentation results of CD-FFD model. (a) IRRG image; (b) DSM image; (c) segmentation result of IRRG-Net; (d) segmentation result of DSM-Net; (e) segmentation result of CD-FFD; (f) ground-truth

Download full size

Fig. 5. Segmentation results of other images by CD-FFD model. (a) Original RGB images; (b) segmentationresults of CD-FFD

Download full size

Type	AverageCOM	Averageglobal acc	AverageIOU
RGB-Net	0.9183	0.9542	0.7815
GRAY-Net	0.9169	0.9407	0.7327
CD-FFD	0.9329	0.9656	0.8188

Table 1. Evaluation results of CD-FFD on 200 validation images from Weizmann Horse

Type	AverageCOM	Averageglobal acc	AverageIOU
RGB-Net	0.9279	0.9672	0.8834
GRAY-Net	0.9360	0.9648	0.8753
CD-FFD	0.9407	0.9708	0.8949

Table 2. Evaluation results of 128 test images from Weizmann horse dataset

Type	AverageCOM	Averageglobal acc	AverageIOU
IRRG-Net	0.9316	0.9668	0.8698
DSM-Net	0.9221	0.9610	0.8420
CD-FFD	0.9496	0.9748	0.8966

Table 3. Evaluation results of 5 test images from vaihigen dataset

Number	Method	Averageglobal acc	AverageIOU
1	Ref. [16]	94.6	80.1
2	Ref. [17]	95.8	84.0
3	Ref. [18]	95.7	84.0
4	Ref. [19]	94.9	79.9
5	CD-FFD	97.2	89.5
6	CD-FFD+CRF	97.6	90.1

Table 4. Comparison among existing results of 128 test images from Weizmann Horse dataset

Number	Method	Average global acc
1	SegNet^[20]	0.9078
2	CNN+RF^[21]	0.9423
3	CNN+RF+CRF^[21]	0.9430
4	Ref. [22]	0.9450
5	CD-FFD	0.9748

Table 5. Comparison among existing results of 5 test images from vaihigen dataset

Chenxiao Feng, Xili Wang. Convolution-Deconvolution Image Segmentation Model for Fusion Features and Decision[J]. Laser & Optoelectronics Progress, 2019, 56(1): 011008

Download Citation

Set citation alerts for the article

Tools

Set citation alerts for the article

Save the article for my favorites

Paper Information