Learning Spatial Context: Using Stuff to Find Things
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1 Learning Spatial Context: Using Stuff to Find Things Wei-Cheng Su
2 Motivation 2 Leverage contextual information to enhance detection Some context objects are non-rigid and are more naturally classified based on texture or color. e.g., sky, trees, road Find the relationships between the stuff of context and the object
3 Outline 3 Training and inferring Preprocessing Experimental results Things-and-stuff relationships Performance Effect of parameters Conclusion
4 Training 4 Segmentation Region features & centroids Learning Detection Candidate boxes & scores Things-and-stuff stuff relationships Model parameters Annotation Ground truths *Red boxes indicate high scores Blue boxes indicate low scores
5 Inferring 5 Segmentation Region features & centroids Inferring Detection Candidate boxes & prior scores Posterior scores for all candidates
6 Outline 6 Training and inferring Preprocessing Experimental results Things-and-stuff relationships Performance Effect of parameters Conclusion
7 Preprocessing 7 Segmentation e Superpixel Pentium-D 2.4 GHz, 4G RAM Run out of memory with a 792x636 image ~6.4 minutes for a 480x321 image Detection HOG for detecting humans, cars, bicycles, and motorbikes Patch-based boosted detector for detecting cars in satellite images
8 Segmentation 8 This level of segmentation result is used
9 9 HoG-Cars
10 HoG-People 10
11 11 HoG-Motorbikes
12 HoG-Bicycles 12
13 13 Satellite
14 Satellite 14 Th=0
15 Satellite 15 Th=0 0.95
16 Satellite 16 Th =
17 Satellite 17 Th=
18 Outline 18 Training and inferring Preprocessing Experimental results Things-and-stuff relationships Performance Effect of parameters Conclusion
19 Running TAS 19 Run TAS inference on all detected candidates False positives detected by the base detector will be filtered out Object not detected by the base detector could not be detected by TAS Data set: VOC2005, Google earth satellite images
20 Base Detector vs TAS 20 Left: base detector result. Right: TAS result
21 21 Base Detector vs TAS
22 22 Base Detector vs TAS
23 23 Base Detector vs TAS
24 24 Base Detector vs TAS
25 25 Base Detector vs TAS
26 26 Base Detector vs TAS
27 27 Base Detector vs TAS
28 28 Base Detector
29 29 TAS
30 30 Base Detector
31 31 TAS
32 32 Base Detector
33 33 TAS
34 Outline 34 Training and inferring Preprocessing Experimental results Things-and-stuff relationships Performance Effect of parameters Conclusion
35 Things-and-Stuff Relationships 35 Feature description: 44 features, including color, texture, shape The relationships are learnt during training The relationships change the score of a candidate 25 relationship candidates
36 Relationships 36
37 Relationships 37
38 Relationships 38
39 Relationships 39
40 Relationships 40
41 Relationships 41
42 Relationships 42
43 Relationships 43
44 Relationships 44
45 Relationships 45
46 Relationships 46
47 Relationships 47
48 Relationships 48
49 Relationships 49
50 Relationships 50
51 Relationships 51
52 Relationships 52
53 Relationships 53
54 Relationships 54
55 Relationships 55
56 Relationships 56 Some regions inside the bounding box have Some regions inside the bounding box have relationships with the candidate
57 Relationships 57 View point. Different viewpoints generate different relationships Region features might be misleading
58 Relationships 58 The diversities of the backgrounds The region features inside the bounding box might be a complementary cue to the features used by the base detector
59 Outline 59 Training and inferring Preprocessing Experimental results Things-and-stuff relationships Performance Effect of parameters Conclusion
60 Performance Analysis 60 Training samples: 15 Test samples: 15 Image size: 792x636 Test machine: Core(TM)2 8G RAM Implemented in Matlab Detection and segmentation are not included Required computing power Learning seconds of CPU time Inferring seconds of CPU time
61 Base Detector vs TAS 61 Cars People Red: base detector. Blue: TAS
62 Base Detector vs TAS - Motorbikes 62 Motorbikes Bicycles Red: base detector. Blue: TAS
63 63 Base Detector vs TAS - Satellite
64 Outline 64 Training and inferring Preprocessing Experimental results Things-and-stuff relationships Performance Effect of parameters Conclusion
65 Number of Region Clusters 65 Red: 10 Blue: 3 Blue: 5 Blue: 20 Blue: 30
66 Number of Gibbs Iterations 66 Red: 10 Blue: 20 Blue: 100
67 Outline 67 Training and inferring Preprocessing Experimental results Things-and-stuff relationships Performance Effect of parameters Conclusion
68 Conclusion 68 Can be easily integrated with detectors The performance is dependent on the detector The stuff can come from the context as well as the object itself Especially suitable for background consistent and view point consistent datasets, ex: aerial images 3D information could be used to improve the performance
69 Reference 69 Learning Spatial Context: Using Stuff to Find Things,, Geremy Heitz and Daphne Koller. European Conference on Computer Vision (ECCV), 2008 TAS Superpixel HOG implemetation i i l / f / l PASCAL VOC soton ac dex.html
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