% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii
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- としなり あみおか
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1 A Graduation Thesis of College of Engineering, Chubu University High Accurate Semantic Segmentation Using Re-labeling Besed on Color Self Similarity Yuko KAKIMI
2 % 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii
3 1 STFs 2 STFs 3 CSS CSS 4 iii
4 Semantic Texton Forests Randomized Trees Semantic Texton Forests Features STFs STFs CSS Color Self-Similarity iv
5 v
6 Semantic Texton Forests Features f f Confusion Matrix CSS STFs CSS CSS CSS CSS f f Confusion Matrix f Confusion Matrix f Confusion Matrix f Confusion Matrix vi
7 4.8 3 f Confusion Matrix f Confusion Matrix vii
8 STFs f [%] f [%] f [%] [%] viii
9 1 STFs 1.1 Bag-of-features 3 He CRF [4] Shotton CRF [5] Winn [7] Tu CRF Auto-Context [8] Scroff Texton HOG [9] Shotton Texton Randomized Trees RTs [3] 1
10 1.2. Semantic Texton Forests STFs[1] Tighe [6] Vezhnevets Geometric Context Multiple Instance Learning Multi-Task Learning [10] 1.2 Semantic Texton Forests Semantic Texton Forests STFs [1] STFs RTs[3] Randomized Trees Randomized Trees RTs [3] Random Forests Randomized Forests Randomized Trees, Randomized Deicision Forests Randomized Trees RTs 1.1 2
11 1.2. Semantic Texton Forests 1.1: 3
12 1.2. Semantic Texton Forests (a) (b) 1.2(b) 1.2: Semantic Texton Forests Features STFs 1.3(b) d d 4 Semantic Texton Forests Features STFF f(p) = p x,y,c (1.1) f(p) = p x1,y 1,c 1 + p x2,y 2,c 2 (1.2) f(p) = p x1,y 1,c 1 p x2,y 2,c 2 (1.3) f(p) = p x1,y 1,c 1 p x2,y 2,c 2 (1.4) 4
13 1.2. Semantic Texton Forests (a) Semantic Texton Forests (b) Semantic Texton Forests Features 1.3: Semantic Texton Forests Features p p x, y c CIELab 1 (1.1) CIELab (b) STFs RTs (a) STFF f(v) i (1.2) (1.4) t. I l = i I n f(v) i < t (1.5) I r = I n \ I i (1.6) I n n I l I r t f(v) i t E. E = I l I n E(I l) I r I n E(I r) (1.7) 5
14 1.2. Semantic Texton Forests 1.7. E(I) = n P (c i ) log 2 P (c i ) (1.8) i=1 P (c i ) l D I l I r P (c l) RTs 6
15 2 STFs STFs grass sky road leaf QVGA( ) : STFs 7
16 : STFs = (2.1) = (2.2) 2 f f 2.3 f = = 2 + (2.3) Confusion Matrix
17 : 9
18 2.3. f f Confusion Matrix 2.3 Confusion Matrix 2.2: f 2.3: f [%] grass sky road leaf STFs STFs f 75%
19 2.4. STFs (a) Confusion Matrix (b) Confusion Matrix 2.4 STFs 2.3: f Confusion Matrix STFs 2.4(a) STFs CSS 2.4(b) 2.4: CSS STFs 11
20 3 CSS CSS CSS 3.1 Color Self-Similarity Color Self-Similarity(CSS) [2] 2 HSV (H: S: V: ) d d HSV 3 b =
21 3.1. Color Self-Similarity 3.1: P 3.1 f = 3 3 (Pb 1 P b 2)2 (3.1) b=1 P b b f S.Walk 13
22 3.1. Color Self-Similarity 3.2: CSS 2 CSS CSS 1 ) : CSS 14
23 3.1. Color Self-Similarity 図 3.4: CSS の可視化 観測パッチ 葉 図 3.5: CSS の可視化 観測パッチ 芝生 図 3.6: CSS の可視化 観測パッチ 空 15
24 3.2. CSS 3.2 STFs CSS CSS HSV 1 n = 1,..., N CSS n QVGA n n i = 1,..., N h i (y) 3.2 h i (y) n STFs y P (y n) i CSS s(i, n) 2 r N h i (y) = s(i, n)p (y n)ω(r)δ[y, STFs(n)] (3.2) n=1 ω STFs(n) n STFs δ[ ]
25 : h i (y) y i 3.3 y i = argmax h i (y) (3.3) y Y 3.8: 17
26 STFs STFs
27 f f road leaf = void (4.1) 19
28 :
29 : 21
30 f f Confusion Matrix : f 4.1: f [%] grass sky road leaf STFs STFs
31 4.4. (a) STFs (b) 4.4: f Confusion Matrix (a) STFs (b) 4.5: f Confusion Matrix 23
32 f STFs 4.2 f Confusion Matrix f 4.2 (a) STFs (b) 4.6: 1 f Confusion Matrix (a) STFs (b) 4.7: 2 f Confusion Matrix 24
33 4.4. (a) STFs (b) 4.8: 3 f Confusion Matrix (a) STFs (b) 4.9: 4 f Confusion Matrix 25
34 : f [%] STFs STFs void STFs 26
35 : 4.3: [%] grass sky road leaf STFs STFs STFs 25.1% 7.3% 27
36 STFs CSS 1 STFs 2 STFs f 75% 3 CSS CSS 4 STFs STFs f STFs STFs void STFs STFs 25.1% 7.3% 28
37 CSS 29
38 30
39 [1] J. Shotton, M. Johnson and R. Cipolla. Semantic Texton Forests for Image Categorization and Segmentation. In Proc. Computer Vision and Pattern Recognition, pp. 1.8, [2] S.Walk and N.Majer New Features and Insights for Pedestrian Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition [3] L.Breiman Random forests, Machine learning, 45, 1, pp [4] X.He, R.S.Zemel, and M.A. Carreira-Perpinan, Multiscaleconditional random fileds for image labeling, Computer Vision and Pattern Recognition, [5] J.Shotton, J.Winn, C. Rother, and A. Criminisi, Textonboost:Joint appearance, shape and context modeling for multi-class object recognition and segmentation, European Conference on Computer Vision, [6] J.Tighe, and S.Lazebnik, Superparsing: Scalable nonparametric image parsing with superpixels, European Conference on Computer Vision, [7] J.Winn, and J. Shotton, The layout consistent random field for recognizing and segmenting partially occluded objects, Computer Vision and Pattern Recognition, [8] Z.Tu, Auto-context and its application to high-level vision tasks, Computer Vision and Pattern Recognition, [9] F.Schroff, A.Criminisi, and A.Zisserman, Object class segmentation using random forests, British Machine Vision Conference,
40 [10] A.Vezhnevets, and J.M.Buhmann, Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning, Computer Vision and Pattern Recognition, pp ,
41 ( )
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Random Forests 2013 3 A Graduation Thesis of College of Engineering, Chubu University Proposal of an efficient feature selection using the contribution rate of Random Forests Katsuya Shimazaki [1] SBS
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