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CVPR 2009, Miami, Florida
Subhransu Maji and Jitendra MalikUniversity of California at Berkeley, Berkeley, CA-94720
Object Detection Using a Max-Margin Hough Transform
Object detection using a max-margin Hough Transform
Overview2
Overview of probabilistic Hough transform Learning framework Experiments Summary
Object detection using a max-margin Hough Transform
Our Approach: Hough Transform Popular for detecting parameterized shapes
Hough’59, Duda&Hart’72, Ballard’81,…
Local parts vote for object pose Complexity : # parts * # votes
Can be significantly lower than brute force search over pose (for example sliding window detectors)
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Object detection using a max-margin Hough Transform
Generalized to object detection
Learning
Spatial occurrence distributionsx
y
s
x
y
sx
y
s
x
y
s
• Learn appearance codebook– Cluster over interest points
on training images
Use Hough space voting to find objects Lowe’99, Leibe et.al.’04,’08, Opelt&Pinz’08
Implicit Shape ModelLeibe et.al.’04,’08
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• Learn spatial distributions– Match codebook to training
images– Record matching positions on
object– Centroid is given
Object detection using a max-margin Hough Transform
Detection Pipeline
Probabilistic Voting
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Interest Points
eg. SIFT,GB, Local Patches
Matched Codebook Entries
KD Tree
B. Leibe, A. Leonardis, and B. Schiele. Combined object categorization and segmentation with an implicit shape model ‘ 2004
Object detection using a max-margin Hough Transform
Probabilistic Hough Transform C – Codebook f – features, l - locations
Position Posterior
Codeword Match
Codeword likelihood
Detection Score
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Codeword likelihood
Object detection using a max-margin Hough Transform
Learning Feature Weights Given :
Appearance Codebook, C Posterior distribution of object center for each codeword P(x|…)
To Do : Learn codebook weights such that the Hough transform detector
works well (i.e. better detection rates) Contributions :
1. Show that these weights can be learned optimally using a max-margin framework.
2. Demonstrate that this leads to improved accuracy on various datasets
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Object detection using a max-margin Hough Transform
Naïve Bayes weights:
Encourages relatively rare parts However rare parts may not be good
predictors of the object location Need to jointly consider both priors and
distribution of location centers.
Learning Feature Weights : First Try
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Object detection using a max-margin Hough Transform
Location invariance assumption Overall score is linear given the matched codebook entries
Position Posterior
Codeword Match
Codeword likelihood
ActivationsFeature weights
Learning Feature Weights : Second Try9
Object detection using a max-margin Hough Transform
Max-Margin Training
Training: 1.Construct dictionary 2.Record codeword distributions on training examples3.Compute “a” vectors on positive and negative training examples4.Learn codebook weights using by max-margin training
Standard ISM model (Leibe
et.al.’04)
Our Contribution
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class label {+1,-1}
activations
non negative
Object detection using a max-margin Hough Transform
Experiment DatasetsETHZ Shape Dataset (Ferrari et al., ECCV 2006) 255 images, over 5 classes (Apple logo, Bottle, Giraffe, Mug, Swan)
UIUC Single Scale Cars Dataset (Agarwal & Roth, ECCV 2002) 1050 training, 170 test images
INRIA Horse Dataset (Jurie & Ferrari) 170 positive + 170 negative images (50 + 50 for training)
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Object detection using a max-margin Hough Transform
Experimental Results
Hough transform details Interest points : Geometric Blur descriptors
at sparse sample of edges (Berg&Malik’01) Codebook constructed using k-means Voting over position and aspect ratio Search over scales
Correct detections (PASCAL criterion)
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Object detection using a max-margin Hough Transform
Max-Margin
Learned Weights (ETHZ shape)
Important Parts
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Naïve Bayes
blue (low) , dark red (high)
Influenced by clutter(rare structures)
Object detection using a max-margin Hough Transform
Learned Weights (UIUC cars)14
Naïve Bayes
Max-Margin
Important Partsblue (low) , dark red
(high)
Object detection using a max-margin Hough Transform
Learned Weights (INRIA horses)
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Naïve Bayes Max-Margin
Important Partsblue (low) , dark red
(high)
Object detection using a max-margin Hough Transform
Detection Results (ETHZ dataset)
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Recall @ 1.0 False Positives Per Window
Object detection using a max-margin Hough Transform
Detection Results (INRIA Horses)
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Our Work
Object detection using a max-margin Hough Transform
Detection Results (UIUC Cars)
INRIA horses
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Our Work
Object detection using a max-margin Hough Transform
Hough Voting + Verification Classifier
ETHZ Shape Dataset IKSVM was run on top 30 windows + local
searchKAS – Ferrari et.al., PAMI’08TPS-RPM – Ferrari et.al., CVPR’07
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Recall @ 0.3 False Positives Per Image
better fitting bounding box
Implicit sampling over aspect-ratio
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Object detection using a max-margin Hough Transform
Hough Voting + Verification Classifier
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IKSVM was run on top 30 windows + local search
Our Work
Object detection using a max-margin Hough Transform
Hough Voting + Verification Classifier
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UIUC Single Scale Car DatasetIKSVM was run on top 10 windows + local
search
1.7% improvement
Object detection using a max-margin Hough Transform
Summary Hough transform based detectors offer good
detection performance and speed. To get better performance one may learn
Discriminative dictionaries (two talks ago, Gall et.al.’09)
Weights on codewords (our work) Our approach directly optimizes detection
performance using a max-margin formulation Any weak predictor of object center can be used
is this framework Eg. Regions (one talk ago, Gu et.al. CVPR’09)
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Work partially supported by:ARO MURI W911NF-06-1-0076 and ONR MURI N00014-06-1-0734
Computer Vision Group @ UC Berkeley
Acknowledgements
Thank You
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Questions?
Object detection using a max-margin Hough Transform
Backup Slide : Toy Example
Rare but poor localization
Rare and good localization
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