Detection Evolution with MultiOrder Contextual Co-Occurrence
Guang Chen (Missouri)
Yuanyuan Ding (Epson)
Jing Xiao (Epson)
Tony Han (Missouri)
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
[Rabinovich et al, 2007][Yao & Fei-Fei, 2010] [Hoiem et al, 2006]
• 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
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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
Image Context + Adaboost
Image
Context
Multi-scale HOGLDP for Each Scan
Window
Contextual Boost
Adaboost
Classification
Classification
Context
Based on
Image Context
Responses at Scale &
Spatial Neighborhood
Adaboost
Classification
Based on
Augmented
Context
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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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Co-Occurrence Context
• Co-Occurrence from Detector
Response Map.
6
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 CoOccurrence (MOCO)
– 0th order: Classification Context
– 1st order: Randomized Binary Comparison
– High order: Co-Occurrence Descriptor
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Constructing MOCO
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0th Order Context
• Directly Using Classifier Responses
Classifier response map (window width=25pixels)
width=100pixels)
width=50pixels)
0th Order Context
• Define Scale and Space Neighborhood
– Spatial (x, y)
– Scale (l)
P
y
l
x
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1st Order Context
• Comparison of Response Values
P
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1st Order Context
• Randomized Arrangement
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High Order Context
• 1. Closeness Vector
• 2. Histogram
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High Order Context
• 3. High Order Representation
– Tensor Product of Normalized Histogram
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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.
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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 CoOccurrence 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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Experiment – 1st Order
• 1st Order & Context Neighbor Size
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Experiment – 1st Order
• Pairwise Comparison: Arrangements
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Experiment – High Order
• High Order Context
– Dimension
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Experiment – Combinations
• Combinations
• Iterations
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Comparison on Caltech Dataset
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Comparison on PASCAL’07
• Mean AP on 20 Categories
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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?
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Questions?
Thanks for your attention!
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