detection evolution with multi- order contextual co-occurrence guang chen (missouri) yuanyuan ding...
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Detection Evolution with Multi-Order Contextual Co-Occurrence
Guang Chen (Missouri)Yuanyuan Ding (Epson)
Jing Xiao (Epson)Tony Han (Missouri)
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Object Detection
• Sliding Window Based Approach– Classifiers and features are
typically inside the window.
• Context Helps– Context outside the sliding window
can be used to achieve better performances.
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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]
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Classification Context for Segmentation
• Spatialboost and Auto-context– Integrate classifier responses from
nearby individual pixels for pixel level segmentation or labeling
Spatial boost [Avidan 2006]Auto-context [Tu & Bai, 2010]
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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 Context
Responses at Scale & Spatial Neighborhood
Adaboost Classification
Based on Augmented
Context
Contextual Boost
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Co-Occurrence Context
• Can we further exploit co-occurrence information given only detectors for a single object type?
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Our Contribution
• An Effective and Efficient Multi-Order Co-Occurrence Context Representation Using a Single Object Detector.
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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
0th Order Context
• Directly Using Classifier Responses
Classifier response map (window width=25pixels)Classifier response map (window width=50pixels)Classifier response map (window width=100pixels)
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Detection Evolution
• Bootstrap training samples using detector responses from the previous iteration.
• Add MOCO context from previous iteration as additional features.
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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
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Experiments
• Datasets– PASCAL VOC 2007, 20 Object
Categories– Caltech Pedestrian
• Deformable-Parts-Model–Default setting ( 3 components,
each with 1 root and 8 part filters)
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Conclusion
• An Efficient Context Representation –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?