sylvia pietzsch chair for image understanding computer science technische universität münchen...

Sylvia Pietzsch

Chair for Image UnderstandingComputer Science

Technische Universität München

pietzsch@in.tum.de

Learning Generic and Person Specific Objective Functions

Diplomarbeit

07.02.2007 2/16Technische Universität MünchenSylvia Pietzsch

Overview Model-based Image Interpretation

Generic Objective Functions Traditional Approach Ideal Objective Functions Learning Objective Functions Experimental Evaluation

Person-specific Objective Functions Experimental Evaluation

07.02.2007 3/16Technische Universität MünchenSylvia Pietzsch

Model-based Image Interpretation Model

Contains a parameter vector p that represents the model‘s configurations.

Objective Function Calculates how well a parameterized model fits to an image.

Fitting Algorithm Searches for the model that fits the image best by minimizing the objective function.

07.02.2007 4/16Technische Universität MünchenSylvia Pietzsch

Traditional Approach

Designer selects salient features from the image and composes them.

Based on designer‘s intuition and implicit knowledge of the domain.

shortcomings: time-consuming resulting objective function is not ideal

07.02.2007 5/16Technische Universität MünchenSylvia Pietzsch

Ideal Objective FunctionsP1: Correctness Property:

The global minimum of the objective function corresponds to the best model fit.

P2: Uni-Modality Property:The objective function has no local extrema or saddle points.

07.02.2007 6/16Technische Universität MünchenSylvia Pietzsch

Example: Comparing Objective Functions

a) image b) along perpendicular c) edge values d) designed objective function e) ideal objective function f) training samples g) learned objective function

07.02.2007 7/16Technische Universität MünchenSylvia Pietzsch

Learning the Objective Function (1)

07.02.2007 8/16Technische Universität MünchenSylvia Pietzsch

Learning the Objective Function (2)

07.02.2007 9/16Technische Universität MünchenSylvia Pietzsch

Learning the Objective Function (3)

6 styles · 3 sizes · (5 · 5) locations = 450 features

07.02.2007 10/16Technische Universität MünchenSylvia Pietzsch

Evaluation 1: Used Features Model trees tend to select the most relevant

features. Edge-based features are hardly used at all.

07.02.2007 11/16Technische Universität MünchenSylvia Pietzsch

Evaluation 2: RobustnessIndicators measure the fulfillment of P1 and P2:I1: Correctness Indicator

Distance between the ideal position of the contour point and the global minimum of the objective function

I2: Uni-Modality Indicator Total number of local minima divided by the size of the considered region

07.02.2007 12/16Technische Universität MünchenSylvia Pietzsch

Evaluation 3: Learning Distance

07.02.2007 13/16Technische Universität MünchenSylvia Pietzsch

Person Specific Objective Functions Single Images

The objective function has to take any appearance of a human face into consideration.

➱ moderate accuracy

Image SequenceThe appearance of a person‘s face only changes slightly. Consider particular characteristics of the visible person,

e.g. beard, glasses, bald head,... ➱ increase of accuracy

Challenges: Learn specific objective functions for groups of persons offline. Detect the correct group online.

07.02.2007 14/16Technische Universität MünchenSylvia Pietzsch

Evaluation: Fitting Results45 persons from news broadcasts on TV

07.02.2007 15/16Technische Universität MünchenSylvia Pietzsch

Outlook Learning objective functions for 3D-Models

Integration of further image features

Compute the image features on the fly

Automatic detection of the visible person:e.g. via AAM parameters

07.02.2007 16/16Technische Universität MünchenSylvia Pietzsch

The End