graz university of technology, austria institute for computer graphics and vision fast visual object...
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![Page 1: Graz University of Technology, AUSTRIA Institute for Computer Graphics and Vision Fast Visual Object Identification and Categorization Michael Grabner,](https://reader030.vdocuments.us/reader030/viewer/2022032800/56649d405503460f94a19ea2/html5/thumbnails/1.jpg)
Graz University of Technology, AUSTRIA
Institute for Computer Graphics and Vision
Fast Visual Object Identification and Categorization
Michael Grabner, Helmut Grabner, Horst Bischof
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 2 (of 19)
Agenda
Motivation
Approach
Experimental Illustration
Results
Outlook
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 3 (of 19)
Problem
Database: Ferencz, Yale, Buffalo
How large scale object recognition can be handled in an adequate time?
How knowledge can be used for incremental learning from few examples?
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 4 (of 19)
Identification vs. Categorization
Faces
Writings
Cars
Horst boring Joe wondering
Bill‘s carZip Code 77840
Horst laughing
Identification Categorization
. . .
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 5 (of 19)
Identification and Categorization
Faces
Horst
Helmut
Joe
Cars
Car 1
Car 2
Car 3
Car 4
Writings
ZIP Codes
Places
wondering
Identification depends on the granularity of categorization
tired
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 6 (of 19)
Our approach
„Object Memory“- Hierarchical meaning objects are stored in a hierarchical way
- Incremental meaning objects can be added incrementally to the structure
- Fast meaning identification of objects is done efficiently
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 7 (of 19)
Features
Two types of features- Haar-Like (Viola and Jones 2001)
- Orientation Histograms
Advantages- Coding of gradient information (Lowe 2004, Edelman 1997)
- Fast computation allows to extract a large number of features leading to robustness (Porikli 2005, Grabner 2005)
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 8 (of 19)
Integral Orientation Histogram
F. Porikli: „Integral histograms: A fast way to extract histograms in Cartesian spaces“, in Proc. CVPR 2005
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 9 (of 19)
Feature Selection
Goal is to distinguish between objects by selecting discriminative features
Feature Pool Learn distance function (Ferencz 2005)
- „same“ vs. „same“ and „same“ vs. „different“
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 10 (of 19)
1.) A weak classifier corresponds to a single feature
2.) Perform boosting to select N features
3.) Final strong classifier is a linear combination of features
Boosting for Feature Selection (Viola and Jones 2001)
selected FeaturesObject model
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 11 (of 19)
Building the „Object Memory“
Initialization: 2 objects form a single layer
Adding a novel object:
- Evaluating the sample starting at the highest layer• If sample can not be modeled by one of the classifiers: ADD TO
CURRENT LAYER
• If sample can be modeled by one of the classifiers: GO DEEPER– If classifier has no child: INITIALIZE A NEW LAYER
Retrain- current layer to distinguish between these models- parents for getting generic object models in higher layers
Generating layers of similar objects and learn to differentiate between these similar objects
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 12 (of 19)
Building the „Object Memory“
Training the Object Memory
On-line Illustration MATLAB
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 13 (of 19)
Identification Process
Evaluating the sample starting at the highest level
Multi-path evaluation based on model confidences
Post Processing (i.e. take reference model with highest confidence)
Note: evaluation is fast using integral data structures
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 14 (of 19)
Identification Process
Evaluation the Object Memory
On-line Illustration MATLAB
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 15 (of 19)
Experiments - Overview
Experiment 1- Illustration of the approach
- 3 categories (Cars, Faces, Writings)
- Training using 6 images per object
- Model complexity: 30 features
Experiment 2- Performance evaluation on category Cars
- Varying number of objects and model complexity
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 16 (of 19)
Experiment 1 – Trained Object Memory
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 17 (of 19)
Experiment 2
Experiment on database Car (Ferencz)
- 6 samples for training (const)
- RPC obtained by varying confidence threshold
Variation of model complexity (30 Objects) Variation of objects (15 Features)
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 18 (of 19)
Conclusion and Outlook
Conclusion- Hierarchical structuring of objects by a simple heuristic
- Incremental adding of novel objects from few examples
- Fast Identification
Outlook- More objects
- Fast and efficient retraining• On-line boosting for model update
- Detection, Tracking and Recognition within one framework• all tasks are performed with same types of features
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NIPS 2005 Workshop: Interclass Transfer„why learning to recognize many objects is easier than learning to recognize just one“
Slide 19 (of 19)
Thank you for your attention!