Andreas Opelt (Graz University of Technology and University of Leoben)

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A. Opelt, M. Fussenegger, A. Pinz, P. Auer

Weak Hypotheses

and Boosting

for Generic Object Detection

and

Recognition

Andreas Opelt (Graz University of Technology and University of Leoben)

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Agenda

• The Basic Idea• Our Framework for generic Object Recognition • The techniques used• The Learning Model

• Our Model• The Weak Hypotheses Finder

• Experiments• Discussion / Outlook

Andreas Opelt (Graz University of Technology and University of Leoben)

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The Basic Idea 1/2

We want to go towards ‘real’ Generic Object Recognition!

No pre-selection of the object !

Arbitrary view of the object!

Any instance of the object category!

Any background clutter!

Object is located anywhere in the

image!

Objects shown in any arbitrary scale!

Not only for a special category of objects!

Not special images for learning!

Problems ?

Andreas Opelt (Graz University of Technology and University of Leoben)

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Agarwal and Roth, ECCV 2002,

Cars side database

The Basic Idea 2/2

We want to go towards ‘real’ Generic Object Recognition!We want to go towards ‘real’ Generic Object Recognition!

Oxford database; (Fergus, Perona and Zisserman, CVPR 2003)

Graz database; Bikes, Persons, Background

Andreas Opelt (Graz University of Technology and University of Leoben)

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The Framework

Andreas Opelt (Graz University of Technology and University of Leoben)

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Region Extraction 1/2

[Mikolajczyk/Schmid 2001]

Data reduction: Threshold

[Mikolajczyk/Schmid 2001]

Data reduction: Threshold

[Lowe 1999] (Diff. of Gaussian)

Data reduction: Clustering

Andreas Opelt (Graz University of Technology and University of Leoben)

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Region Extraction 2/2

Andreas Opelt (Graz University of Technology and University of Leoben)

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Region Normalization

• Homomorphic Filtering [Gonzales and Woods, C. 4.5.]

• Size Normalization

Andreas Opelt (Graz University of Technology and University of Leoben)

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The Framework

Andreas Opelt (Graz University of Technology and University of Leoben)

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Local Descriptors

Subsampled Grayvalues

Basic Moments (Dim=10)

[L. Van Gool 1996]

Dim=9

[D. Lowe 1999]

Dim=128 (3 orient. planes, 8x8px)

Andreas Opelt (Graz University of Technology and University of Leoben)

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The Framework

Andreas Opelt (Graz University of Technology and University of Leoben)

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The Learning Model 1/3

Input:

Output:

Weak Hypotheses:

Threshold, Weight

Andreas Opelt (Graz University of Technology and University of Leoben)

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The Learning Model 1/3

Select best Weak Hypothesis

Calculate Threshold

Andreas Opelt (Graz University of Technology and University of Leoben)

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The Learning Model 3/3

Andreas Opelt (Graz University of Technology and University of Leoben)

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Experiments 1/6

Category: Bikes some Weak Hypotheses

Andreas Opelt (Graz University of Technology and University of Leoben)

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Experiments 2/6

Testing

BIKE !

Andreas Opelt (Graz University of Technology and University of Leoben)

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Experiments 3/6

Testing

BIKE ! BIKE !

Andreas Opelt (Graz University of Technology and University of Leoben)

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Experiments 4/6

Testing

NO BIKE ! NO BIKE !

Andreas Opelt (Graz University of Technology and University of Leoben)

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Experiments 5/6

Testing

NO BIKE !

Andreas Opelt (Graz University of Technology and University of Leoben)

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Experiments 6/6

Facts:

Andreas Opelt (Graz University of Technology and University of Leoben)

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Discussion / Outlook

• Further Experimental Evaluation

• Multiclass Categorisation

• Combination with other Types of Regions

Andreas Opelt (Graz University of Technology and University of Leoben)

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Conclusion

• Generic object recognition

• A new Framework

• A new Learning Model

• Good Results

Andreas Opelt (Graz University of Technology and University of Leoben)

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Thank you !

Generic object recognition; not an easy task!

Thanks to the Lava Project and the FWF Project – FSP Cognitive Vision