Superpixel Segmentation using Depth Information David Stutz - PowerPoint PPT Presentation

Evaluation Evaluation Used datasets: – Berkeley Segmentation Dataset (BSDS500) [AMFM11]: 500 natural images. – NYU Depth Dataset (NYUV2) [SHKF12]: 1449 images of indoor scenes with depth information. Figure: Images and corresponding ground truth segmentations from the BSDS500 and the NYUV2. David Stutz | October 7th, 2014 31

Evaluation Evaluation Parameters have been optimized on training sets with respect to: – Boundary Recall Rec : the fraction of boundary pixels in the ground truth segmentation correctly detected in the superpixel segmentation. → 100% is best. – Undersegmentation Error UE : the error made when comparing the ground truth segmentation to the superpixel segmentation. → 0% is best. Qualitative and quantitative comparison on test sets. David Stutz | October 7th, 2014 32

Evaluation Evaluation Parameters have been optimized on training sets with respect to: – Boundary Recall Rec : the fraction of boundary pixels in the ground truth segmentation correctly detected in the superpixel segmentation. → 100% is best. – Undersegmentation Error UE : the error made when comparing the ground truth segmentation to the superpixel segmentation. → 0% is best. Qualitative and quantitative comparison on test sets. David Stutz | October 7th, 2014 32

Evaluation Evaluation Parameters have been optimized on training sets with respect to: – Boundary Recall Rec : the fraction of boundary pixels in the ground truth segmentation correctly detected in the superpixel segmentation. → 100% is best. – Undersegmentation Error UE : the error made when comparing the ground truth segmentation to the superpixel segmentation. → 0% is best. Qualitative and quantitative comparison on test sets. David Stutz | October 7th, 2014 32

Evaluation • Qualitative Table of Contents 1 Introduction Goals 2 Related Work 3 SEEDS 4 SEEDS with Depth 5 Evaluation 6 Qualitative Quantitative Runtime Conclusion 7 David Stutz | October 7th, 2014 33

Evaluation • Qualitative Qualitative Comparison – FH Figure: Superpixel segmentations generated by FH . David Stutz | October 7th, 2014 34

Evaluation • Qualitative Qualitative Comparison – SLIC Figure: Superpixel segmentations generated by SLIC . David Stutz | October 7th, 2014 35

Evaluation • Qualitative Qualitative Comparison – oriSEEDS Figure: Superpixel segmentations generated by oriSEEDS . David Stutz | October 7th, 2014 36

Evaluation • Qualitative Qualitative Comparison – reSEEDS* Figure: Superpixel segmentations generated by reSEEDS* . David Stutz | October 7th, 2014 37

Evaluation • Qualitative Qualitative Comparison – SEEDS3D Figure: Superpixel segmentations generated by SEEDS3D . David Stutz | October 7th, 2014 38

Evaluation • Qualitative Qualitative Comparison – VCCS Figure: Superpixel segmentations generated by VCCS . David Stutz | October 7th, 2014 39

Evaluation • Quantitative Table of Contents 1 Introduction Goals 2 Related Work 3 SEEDS 4 SEEDS with Depth 5 Evaluation 6 Qualitative Quantitative Runtime Conclusion 7 David Stutz | October 7th, 2014 40

Evaluation • Quantitative Quantitative Comparison – BSDS500 1 0 . 1 BSDS500: oriSEEDS 0 . 98 reSEEDS* 0 . 08 0 . 96 Rec UE 0 . 06 0 . 94 0 . 92 0 . 04 0 . 9 0 . 03 500 1 , 000 500 1 , 000 Superpixels Superpixels David Stutz | October 7th, 2014 41

Evaluation • Quantitative Quantitative Comparison – BSDS500 1 0 . 1 BSDS500: SLIC 0 . 98 oriSEEDS 0 . 08 reSEEDS* 0 . 96 Rec UE 0 . 06 0 . 94 0 . 92 0 . 04 0 . 9 0 . 03 500 1 , 000 500 1 , 000 Superpixels Superpixels David Stutz | October 7th, 2014 42

Evaluation • Quantitative Quantitative Comparison – BSDS500 1 0 . 1 BSDS500: FH 0 . 98 SLIC 0 . 08 oriSEEDS reSEEDS* 0 . 96 Rec UE 0 . 06 0 . 94 0 . 92 0 . 04 0 . 9 0 . 03 500 1 , 000 500 1 , 000 Superpixels Superpixels David Stutz | October 7th, 2014 43

Evaluation • Quantitative Quantitative Comparison – NYUV2 1 0 . 19 NYUV2: 0 . 18 oriSEEDS 0 . 98 reSEEDS* 0 . 16 SEEDS3D 0 . 14 0 . 96 Rec UE 0 . 12 0 . 94 0 . 1 0 . 92 0 . 08 0 . 91 0 . 07 500 1 , 000 1 , 500 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 44

Evaluation • Quantitative Quantitative Comparison – NYUV2 1 0 . 19 NYUV2: 0 . 18 FH SLIC 0 . 98 0 . 16 oriSEEDS reSEEDS* 0 . 14 0 . 96 SEEDS3D Rec UE 0 . 12 0 . 94 0 . 1 0 . 92 0 . 08 0 . 91 0 . 07 500 1 , 000 1 , 500 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 45

Evaluation • Quantitative Quantitative Comparison – NYUV2 1 0 . 19 NYUV2: 0 . 18 FH SLIC 0 . 98 0 . 16 oriSEEDS reSEEDS* 0 . 14 0 . 96 SEEDS3D Rec UE VCCS 0 . 12 0 . 94 0 . 1 0 . 92 0 . 08 0 . 91 0 . 07 500 1 , 000 1 , 500 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 46

Evaluation • Runtime Table of Contents 1 Introduction Goals 2 Related Work 3 SEEDS 4 SEEDS with Depth 5 Evaluation 6 Qualitative Quantitative Runtime Conclusion 7 David Stutz | October 7th, 2014 47

Evaluation • Runtime Comparison – Runtime Runtime is an important aspect, especially for realtime applications. Runtime (in seconds) based on: – i7 @ 3.4GHz with 16GB RAM. – No multi-threading and no GPU. Pixel counts: – BSDS500: 481 · 321 = 154401 pixels. – NYUV2: 608 · 448 = 272384 pixels. David Stutz | October 7th, 2014 48

Evaluation • Runtime Comparison – Runtime BSDS500 NYUV2 0 . 35 0 . 45 oriSEEDS 0 . 4 0 . 3 reSEEDS* SEEDS3D 0 . 3 0 . 2 t t 0 . 2 0 . 1 0 . 1 0 . 05 0 . 05 0 0 500 1 , 000 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 49

Evaluation • Runtime Comparison – Runtime BSDS500 NYUV2 0 . 35 0 . 45 FH 0 . 4 0 . 3 SLIC oriSEEDS reSEEDS* 0 . 3 SEEDS3D 0 . 2 t t 0 . 2 0 . 1 0 . 1 0 . 05 0 . 05 0 0 500 1 , 000 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 50

Evaluation • Runtime Comparison – Runtime BSDS500 NYUV2 0 . 35 0 . 45 FH 0 . 4 0 . 3 SLIC oriSEEDS reSEEDS* 0 . 3 SEEDS3D 0 . 2 VCCS t t 0 . 2 0 . 1 0 . 1 0 . 05 0 . 05 0 0 500 1 , 000 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 51

Evaluation • Runtime Comparison – Runtime – Discussion FH is pretty fast with ∼ 60 ms on the BSDS500. – Cannot be sped up further. However, SLIC and SEEDS can be sped up: – SLIC and SEEDS run iteratively. → Reduce number of iterations T . – Reduce the size Q of the color histograms used by SEEDS . David Stutz | October 7th, 2014 52

Evaluation • Runtime Comparison – Runtime BSDS500 NYUV2 0 . 35 0 . 45 T = 10 : 0 . 4 0 . 3 SLIC T = 2 , Q = 7 3 : oriSEEDS 0 . 2 reSEEDS* t t 0 . 2 0 . 1 0 . 1 0 . 05 0 . 05 0 . 03 0 0 500 1 , 000 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 53

Evaluation • Runtime Comparison – Runtime BSDS500 NYUV2 0 . 35 0 . 45 T = 10 : 0 . 4 0 . 3 SLIC T = 1 : SLIC T = 2 , Q = 7 3 : 0 . 2 t t oriSEEDS 0 . 2 reSEEDS* T = 1 , Q = 3 3 : 0 . 1 0 . 1 oriSEEDS 0 . 05 0 . 05 reSEEDS* 0 . 03 0 0 500 1 , 000 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 54

Evaluation • Runtime Comparison – Runtime 1 0 . 12 BSDS500: T = 10 : 0 . 98 0 . 1 SLIC T = 2 , Q = 7 3 : 0 . 96 oriSEEDS 0 . 08 Rec reSEEDS* UE 0 . 94 0 . 06 0 . 92 0 . 04 0 . 9 0 . 03 500 1 , 000 500 1 , 000 Superpixels Superpixels David Stutz | October 7th, 2014 55

Evaluation • Runtime Comparison – Runtime 1 0 . 12 BSDS500: T = 10 : 0 . 98 0 . 1 SLIC T = 1 : SLIC 0 . 96 0 . 08 T = 2 , Q = 7 3 : Rec UE oriSEEDS 0 . 94 reSEEDS* 0 . 06 T = 1 , Q = 3 3 : 0 . 92 oriSEEDS 0 . 04 reSEEDS* 0 . 9 0 . 03 500 1 , 000 500 1 , 000 Superpixels Superpixels David Stutz | October 7th, 2014 56

Evaluation • Runtime Comparison – Runtime 1 0 . 18 NYUV2: T = 10 : 0 . 98 0 . 16 SLIC T = 2 , Q = 7 3 : 0 . 96 0 . 14 oriSEEDS Rec reSEEDS* UE 0 . 94 0 . 12 0 . 92 0 . 1 0 . 9 0 . 08 500 1 , 000 1 , 500 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 57

Evaluation • Runtime Comparison – Runtime 1 0 . 18 NYUV2: T = 10 : 0 . 98 0 . 16 SLIC T = 1 : SLIC 0 . 96 0 . 14 T = 2 , Q = 7 3 : Rec UE oriSEEDS 0 . 94 0 . 12 reSEEDS* T = 1 , Q = 3 3 : 0 . 92 0 . 1 oriSEEDS reSEEDS* 0 . 9 0 . 08 500 1 , 000 1 , 500 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 58

Conclusion Table of Contents 1 Introduction Goals 2 Related Work 3 SEEDS 4 SEEDS with Depth 5 Evaluation 6 Qualitative Quantitative Runtime Conclusion 7 David Stutz | October 7th, 2014 59

Conclusion Conclusion – First Part The conclusion is split up into three observations. Conclusion 1: Our implementation of SEEDS offers state-of-the-art performance while providing realtime! In addition: – Number of superpixels is controllable. – Compactness is adjustable. – Allows to trade performance for runtime. David Stutz | October 7th, 2014 60

Conclusion Conclusion – Second Part Conclusion 2: Using depth information for superpixel segmentation does not show significant performance increase. – At least for SEEDS . Possible explanations: – Performance of SEEDS leaves little room for improvement. – Scenes from the NYUV2 are highly cluttered and provided depth images have low quality. David Stutz | October 7th, 2014 61

Conclusion Conclusion – Third Part Conclusion 3: Many superpixel algorithms show state-of-the-art performance. Therefore, other aspects become important: – Runtime – Ease-of-use (implementation, parameters etc.) – Control over the number of superpixels – Compactness parameter Based on these considerations, our implementation of SEEDS is an excellent choice. David Stutz | October 7th, 2014 62

Conclusion Conclusion – Third Part Conclusion 3: Many superpixel algorithms show state-of-the-art performance. Therefore, other aspects become important: – Runtime – Ease-of-use (implementation, parameters etc.) – Control over the number of superpixels – Compactness parameter Based on these considerations, our implementation of SEEDS is an excellent choice. David Stutz | October 7th, 2014 62

Conclusion The End – Thanks Thank you for your attention. david.stutz@rwth-aachen.de Questions? David Stutz | October 7th, 2014 63

Appendix Appendix – SEEDS Input: image I , block size w × h , levels L , histogram size Q Output: superpixel segmentation S 1. // Initialization: 2. group w × h pixels to form blocks at level l = 1 3. for l = 2 to L 4. group 2 × 2 blocks at level ( l − 1) to form blocks at level l 5. for l = 1 to L 6. // For l = L , these are the initial superpixels. for each block B ( l ) 7. at level l i i ( q ) is the fraction of pixels in B ( l ) 8. // h B ( l ) falling in bin q . i 9. compute color histogram h B ( l ) i David Stutz | October 7th, 2014 64

Appendix Appendix – SEEDS Input: image I , block size w (1) × h (1) , levels L , histogram size Q Output: superpixel segmentation S 10. // Block updates: 11. for l = L − 1 to 1 for each block B ( l ) 12. at level l i let S j be the superpixel B ( l ) 13. belongs to i 14. if a neighboring block belongs to a different superpixel S k // ∩ ( h, h ′ ) = � Q 15. q =1 min( h ( q ) , h ′ ( q )) . 16. then if ∩ ( h B ( l ) i , h S k ) > ∩ ( h B ( l ) i ) i , h S j − B ( l ) then assign B ( l ) 17. to superpixel S k i David Stutz | October 7th, 2014 65

Appendix Appendix – SEEDS Input: image I , block size w (1) × h (1) , levels L , histogram size Q Output: superpixel segmentation S 18. // Pixel updates: 19. for n = 1 to N 20. let S j be the superpixel x n belongs to 21. if a neighboring pixel belongs to a different superpixel S k 22. // h ( x n ) denotes the bin of pixel x n . 23. then if h S k ( h ( x n )) > h S j ( h ( x n )) 24. then assign x n to superpixel S k 25. return S David Stutz | October 7th, 2014 66

Appendix Appendix – SEEDS Input: image I , block size w (1) × h (1) , levels L , histogram size Q Output: superpixel segmentation S 19. // Mean pixel updates: 20. for n = 1 to N 21. let S j be the superpixel x n belongs to 22. if a neighboring pixel belongs to a different superpixel S k 23. // d ( x n , S j ) = � I ( x n ) − I ( S j ) � 2 + β � x n − µ ( S j ) � 2 . 24. then if d ( x n , S k ) < d ( x n , S j ) 25. then assign x n to superpixel S k 26. return S David Stutz | October 7th, 2014 67

Appendix Appendix – Comparison – BSDS500 1 0 . 1 NYUV2: FH 0 . 98 TP 0 . 08 SLIC ERS 0 . 96 oriSEEDS Rec UE reSEEDS* 0 . 06 0 . 94 0 . 92 0 . 04 0 . 91 0 . 03 500 1 , 000 500 1 , 000 Superpixels Superpixels David Stutz | October 7th, 2014 68

Appendix Appendix – Comparison – NYUV2 1 0 . 19 NYUV2: 0 . 18 FH TP 0 . 98 0 . 16 SLIC ERS 0 . 14 0 . 96 oriSEEDS Rec UE reSEEDS* 0 . 12 SEEDS3D 0 . 94 DASP 0 . 1 VCCS 0 . 92 0 . 08 0 . 91 0 . 07 500 1 , 000 1 , 500 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 69

Appendix Appendix – Runtime It can be shown that SEEDS runs linear in the number of pixels N : (1) O ( QTN ) with – Q the number of histogram bins, – T the number of iterations at each level. However, in practice, the runtime also depends on the number of levels L ! David Stutz | October 7th, 2014 70

Appendix Appendix – Runtime It can be shown that SEEDS runs linear in the number of pixels N : (1) O ( QTN ) with – Q the number of histogram bins, – T the number of iterations at each level. However, in practice, the runtime also depends on the number of levels L ! David Stutz | October 7th, 2014 70

Appendix Appendix – Runtime BSDS500 NYUV2 0 . 35 0 . 45 T = 10 : 0 . 4 0 . 3 SLIC T = 1 : SLIC T = 2 , Q = 7 3 : 0 . 2 t t oriSEEDS 0 . 2 reSEEDS* T = 1 , Q = 3 3 : 0 . 1 0 . 1 oriSEEDS 0 . 05 0 . 05 reSEEDS* 0 . 03 0 0 500 1 , 000 500 1 , 000 1 , 500 Superpixels Superpixels David Stutz | October 7th, 2014 71

Appendix Appendix – Boundary Recall Let G be a ground truth segmentation and S be a superpixel segmentation. Some definitions [NP12]: – True Positives TP ( G, S ) : The number of boundary pixels in G for which there is a boundary pixel in S in range r . – False Negatives FN ( G, S ) : The number of boundary pixels in G for which there is no boundary pixel in S in range r . Boundary Recall is defined as TP ( G, S ) (2) Rec ( G, S ) = TP ( G, S ) + FN ( G, S ) . David Stutz | October 7th, 2014 72

Appendix Appendix – Undersegmentation Error Let G be a ground truth segmentation, S be a superpixel segmentation and N be the total number of pixels. Undersegmentation Error is defined as   UE ( G, S ) = 1  � �  . (3) min( | S j ∩ G i | , | S j − G i | ) N G i ∈ G S j ∩ G i � = ∅ David Stutz | October 7th, 2014 73

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