Improved GrabCut Algorithm Based on Probabilistic Neural Network

Fig. 3. Grayscale histograms of foreground and background. (a) Original image; (b) grayscale histogram of the foreground; (c) grayscale histogram of the background

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Fig. 4. Flow chart of PNN_GrabCut algorithm

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Fig. 5. Experimental example. (a) Original image; (b) label image

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Fig. 6. Segmentation results of different algorithms. (a) Original image; (b) GrabCut algorithm; (c) PNN_GrabCut algorithm; (d) algorithm of Ref. [12]; (d) algorithm of Ref. [16]

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Fig. 7. Segmentation results of different algorithms. (a) Original image; (b) GrabCut algorithm; (c) PNN_GrabCut algorithm

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Table 1. Statistics of the high pixel value in foreground
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Table 1. Statistics of the high pixel value in foreground
Pixel No. 190-194 195-199 185-199 180-184 40-44 45-49 175-179 155-159
Amount 1418 1372 1020 584 405 354 317 272

Table 2. Statistics of the high pixel value in background
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Table 2. Statistics of the high pixel value in background
Pixel No. 160-164 165-169 175-179 155-159 170-174 180-184 150-154 145-149
Amount 3224 2304 2253 2246 2176 1330 1144 414

Table 3. Experimental data
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Table 3. Experimental data
Class Train set Validation set
Person 5075 526
Plane 135 12

Table 4. PNN prediction results at different σ(experiment1)
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Table 4. PNN prediction results at different σ(experiment1)
σ n P/%
0.0005 153 2.94
0.001 631 12.11
0.005 2413 46.31
0.01 766 14.70
0.05 509 9.77
0.1 540 10.37
0.5 198 3.80

Table 5. PNN prediction results at different σ (experiment2)
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Table 5. PNN prediction results at different σ (experiment2)
σ n P/%
0.003 191 7.92
0.004 379 15.70
0.005 1206 49.98
0.006 435 18.03
0.007 202 8.37

Table 6. Average F₁and running time of different algorithms
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Table 6. Average F₁and running time of different algorithms
Algorithm Average value of F₁ Average time F₁ increase rate/% Time increase rate/%
GrabCut 0.809 6.501 / /
PNN_GrabCut 0.857 5.229 5.93 19.57
Ref. [12] 0.827 4.728 2.22 27.27
Ref. [16] 0.846 5.419 4.57 16.64