0. Detection Evolution with Multi-Order Contextual Co-Occurrence
Guang Chen (Missouri)
Yuanyuan Ding (Epson)
Jing Xiao (Epson)
Tony Han (Missouri)

1. Object Detection Sliding Window Based Approach Context Helps
Classifiers and features are typically inside the window.
Context Helps
Context outside the sliding window can be used to achieve better performances.

2. Context in Computer Vision
High Level Context
Semantic Context
Geometric Context
Low Level Context
Pixel Context
Shape Context
Murphy et al, 2003
Hoiem et al, 2006
Avidan, 2006
Shotton et al, 2006
Rabinovich et al, 2007
Oliva & Torralba, 2007
Heitz & Koller, 2008
Desai et al, 2009
Divvala et al, 2009
Li, Socher & Fei-Fei, 2009
Marszalek et al, 2009
Bao & Savarese, 2010
Yao & Fei-Fei, 2010
Tu & Bai, 2010
Li, Parikh & Chen, 2011
Wolf & Bileschi, 2006
Belongie et al, 2000
[Rabinovich et al, 2007]
[Yao & Fei-Fei, 2010]
[Hoiem et al, 2006]

3. Classification Context for Segmentation
Spatialboost and Auto-context
Integrate classifier responses from nearby individual pixels for pixel level segmentation or labeling
Auto-context [Tu & Bai, 2010]
Spatial boost [Avidan 2006]

4. Classification Context for Object Detection
Contextual Boost [Ding & Xiao, 2012]
Directly uses the detector responses
Adaboost Classification
Based on
Image Context
Image Context + Adaboost
Image
Context
Multi-scale HOG-LDP for Each Scan Window
Classification
Responses at Scale & Spatial Neighborhood
Based on Augmented Context
Contextual Boost

5. Co-Occurrence Context
Can we further exploit co-occurrence information given only detectors for a single object type?

6. Co-Occurrence Context
Co-Occurrence from Detector Response Map.

7. Our Contribution
An Effective and Efficient Multi-Order Co-Occurrence Context Representation Using a Single Object Detector.

8. Our Contribution
An Effective and Efficient Multi-Order Co-Occurrence Context Representation Using a Single Object Detector.
Multi-Order Contextual Co-Occurrence (MOCO)
0th order: Classification Context
1st order: Randomized Binary Comparison
High order: Co-Occurrence Descriptor

9. Constructing MOCO

10. 0th Order Context Directly Using Classifier Responses
Classifier response map (window width=100pixels)
Classifier response map (window width=25pixels)
Classifier response map (window width=50pixels)

11. 0th Order Context Define Scale and Space Neighborhood P Spatial (x, y)
Scale (l) P y x l

12. 1st Order Context
Comparison of Response Values P

13. 1st Order Context
Randomized Arrangement

14. High Order Context
1. Closeness Vector
2. Histogram

15. High Order Context 3. High Order Representation
Tensor Product of Normalized Histogram

16. Detection Evolution
Bootstrap training samples using detector responses from the previous iteration.
Add MOCO context from previous iteration as additional features.

17. Baseline Detector
Any Object Detection Algorithm Can be Used as Baseline Detector.

18. Baseline Detector
Any Object Detection Algorithm Can be Used as Baseline Detector.
Deformable-Parts-Model [Felzenszwalb et al, 2010]
Inner Context: Parts Models Encodes Relationship between Parts.
Outer Context: MOCO deals with Co-Occurrence among Scanning Windows

19. Experiments Datasets Deformable-Parts-Model
PASCAL VOC 2007, 20 Object Categories
Caltech Pedestrian
Deformable-Parts-Model
Default setting ( 3 components, each with 1 root and 8 part filters)

20. Experiment – 1st Order
1st Order & Context Neighbor Size

21. Experiment – 1st Order
Pairwise Comparison: Arrangements

22. Experiment – High Order
High Order Context
Dimension

23. Experiment – Combinations
Iterations

24. Comparison on Caltech Dataset

25. Comparison on PASCAL’07
Mean AP on 20 Categories

26. Conclusion An Efficient Context Representation Future Work
Only Relying on Detectors for a Single Object Type
Combining Deformable Parts Model to Model both inner and Outer Context around Detection Window
Future Work
Exploit Context With Detectors of Multiple Object Types?

27. Thanks for your attention!
Questions?
Thanks for your attention!