1. Graz University of Technology, AUSTRIA Institute for Computer Graphics and Vision Fast Visual Object Identification and Categorization Michael Grabner, Helmut Grabner, Horst Bischof

2. 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

3. 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?

4. 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 boringJoe wondering Bill‘s carZip Code 77840 Horst laughing Identification Categorization...

5. 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

6. 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

7. 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)

8. 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

9. 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“

10. 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 Features Object model

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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)

18. 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

19. 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!