Part-Based Models for Object Class Recognition Part 3
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1 High Level Computer Vision! Part-Based Models for Object Class Recognition Part 3 Bernt Schiele - schiele@mpi-inf.mpg.de Mario Fritz - mfritz@mpi-inf.mpg.de!
2 ! State-of-the-Art in Object Class Representations Bag of Words Models (BoW) object model = histogram of local features e.g. local feature around interest points Global Object Models object model = global feature object feature e.g. HOG (Histogram of Oriented Gradients) Part-Based Object Models object model = models of parts & spatial topology model e.g. constellation model or ISM (Implicit Shape Model) But: What is the Ideal Notion of Parts here? And: Should those Parts be Semantic? BoW: no spatial relationships e.g. HOG: fixed spatial relationships e.g. ISM: flexible spatial relationships High Level Computer Vision - July o2, 2o14 2
3 Part-Based Models - Overview So Far Previous lectures about part-based models: Part-Based based on Manual Labeling of Parts - Detection by Components, Multi-Scale Parts The Constellation Model - automatic discovery of parts and part-structure The Implicit Shape Model (ISM) - star-model of part configurations, parts obtained by clustering interest-points Pictorial Structures Model Hierarchical Graphical Model Learning Object Model from CAD Data Discussion Semantic Parts vs. Discriminative Parts High Level Computer Vision - July o2, 2o14 3
4 Part-Based Models - Overview Today! Deformable Parts Model (DPM) considered as THE best model at the moment!! Extensions 3D - DPM grammar-based detection High Level Computer Vision - July o2, 2o14 4
5 slide curtesy: Pedro Felzenszwalb High Level Computer Vision - July o2, 2o14 5
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18 Efficient Computation Overall score: score(p! 0,...,p n )=! nx F i (H, p i ) i=0 Maximization can be done separately: nx i=1 d i (dx 2 i,dy 2 i ) score(p 0 ) = max score(p 0,...,p n ) p 1,...,p n = F 0 (H, p 0 )+ max p 1 F 1 (H, p 1 ) d 1 (dx 2 1,dy 2 1) + + max p n F n (H, p n ) d n (dx 2 n,dy 2 n) High Level Computer Vision - July o2, 2o14 18
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23 SVM training Classifier scores and example x using:! model parameter: feature vector: (x)!! Linear SVM: objective: maximize margin (for best generalization) f(x) = (x) margin High Level Computer Vision - July o2, 2o14 23
24 SVM training Training data:!! Constraints:!!!!! D =(hx 1,y 1 i,...,hx n,y n i) with y i 2 { 1, 1} f(x i ) +1 for y i =+1 f(x i ) apple 1 for y i = 1 ) y i f(x i ) +1 ) 0 1 y i f(x i ) Training error: nx max (0, 1 y i f(x i )) i=1 margin High Level Computer Vision - July o2, 2o14 24
25 SVM training Two objectives: maximize margin: minimize training error:! Therefore minimize (primal formulation)!! L( )=min ! Hinge loss: H(z) = max(0, 1 z) min nx min max (0, 1 y i f(x i )) i=1! nx max (0, 1 y i f(x i )) i=1 0 z High Level Computer Vision - July o2, 2o14 25
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33 slides from Dan Huttenlocher High Level Computer Vision - July o2, 2o14 33
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44 Part-Based Models - Overview Today Deformable Parts Model (DPM) considered as THE best model at the moment!! Extensions 3D - DPM grammar-based detection High Level Computer Vision - July o2, 2o14 44
45 Teaching 3D Geometry to Deformable Part Models Bojan Pepikj 1 1 Max Planck Institute for Informatics!! Michael Stark 1,2! 2 Stanford University! Peter Gehler 3! Bernt Schiele 1! 3 Max Planck Institute for Intelligent Systems! High Level Computer Vision - July o3, 2o13 45
46 Deformable Part part model Deformable Model (DPM) recap Recap Structure: mixture of star models = ( 1,..., M ) = ( 1, 2..., M ) Mixture of star root templates part templates car components part templates root template car deformations deformations M M Fig. 9. Some of the models learned on the PASCAL 2007 dataset. Fig. 9. Some of the models learned on the PASCAL 2007 dataset. unary terms Detection: best joint template placement Inference: best matching component pairwise terms (y, h ) = argmax, (I, y, h) (y,h) (y,h) bounding box bounding box hypothesis hypothesis MPC-VCC Review image features image features part positions component selection, part positions [Felsenszwalb et [Felsenszwalb et al. al. PAMI 10] PAMI 09] Visual Recognition Scene Interpretation High Object Level Computer Visionand - July o3, 2o13 Michael Stark 10 46
47 3D part parameterization Independent parts across components ? M Part correspondences across object views High Level Computer Vision - July o3, 2o13 47
48 3D part parameterization DPM parts defined in image plane P 1... P M? M Parameterize parts in 3D object coordinates 3D parts linked across components High Level Computer Vision - July o3, 2o13 48
49 3D part learning How to infer consistent 3D parts? Per object instance (not per image) 3D parts are observed by viewpoint-specific components DPM-3D-Constraints P 1 P M P 1 (x,y,z) (x,y,z) P M (x,y,z) = h, (I 1,y 1,h 1 )i h, (I M,y M,h M )i optimal 3D part placement High Level Computer Vision - July o3, 2o13 49
50 3D part parameterization - implementation Computed aided design (CAD) data Render images from arbitrary views Accurate supervision Non-photorealistic rendering [Stark et al. BMVC 10] High Level Computer Vision - July o3, 2o13 50
51 Results - 3D part parameterization Task: Object localization and viewpoint classification 3D object classes [Savarese & Fei-Fei ICCV 07] DPM-VOC+VP [Lopez et al. CORP 11] Cars AP Object localization DPM-3D-Constraints [Glasner et al. ICCV 11] Bicycles DPM-VOC+VP DPM-3D-Constraints [Payet et al. ICCV 11] MPPE +10.2% Cars Bicycles Viewpoint classification +15.6% DPM-VOC+VP slightly better than DPM-3D-Constraints DPM-3D-Constraints and DPM-VOC+VP improve over state-of-the-art High Level Computer Vision - July o3, 2o13 51
52 Results - 3D part parameterization Quantitative analysis: Ultra-wide baseline matching experiment [Zia et al. 3dRR 11] Fundamental matrix estimation 140 image pairs % correct SIFT [Zia et al. 3dRR 11] DPM-3D-Constraints + 5.1% Average improvement of 5.1% High Level Computer Vision - July o3, 2o13 52
53 EPFL Multi-view cars [Ozuysal et al. CVPR 09] Part correspondences without any temporal smoothing High Level Computer Vision - July o3, 2o13 53
54 Part-Based Models - Overview Today Deformable Parts Model (DPM) considered as THE best model at the moment!! Extensions 3D - DPM grammar-based detection High Level Computer Vision - July o2, 2o14 54
55 slide courtesy: again Pedro Felzenswalb High Level Computer Vision - July o2, 2o14 55
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72 person with varying occlusion different part types different occlusion types per part type: full DPM-model with root & parts High Level Computer Vision - July o2, 2o14 72
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76 Part-Based Models - References Today Deformable Parts Model (DPM) "Object Detection with Discriminatively Trained Part-Based Models" P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. IEEE Pattern Analysis and Machine Intelligence (PAMI). Sept D - DPM Teaching 3D Geometry to Deformable Part Models, B. Pepik, M. Stark, P. Gehler and B. Schiele, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012 not covered today: 3D 2 PM - 3D Deformable Part Models, B. Pepik, P. Gehler, M. Stark and B. Schiele, European Conference on Computer Vision (ECCV) 2012 grammar-based detection Object Detection with Grammar Models R. Girshick, P. Felzenszwalb, D. McAllester Neural Information Processing Systems, NIPS 2011 High Level Computer Vision - July o2, 2o14 76
Part-Based Models for Object Class Recognition Part 2
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Part-Based Models for Object Class Recognition Part 2
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