D31 Entity RecognitionResults with Auto-

associative Memories

Nicolas GourierINRIAPRIMA TeamGRAVIR

Laboratory

CAVIAR Project

Entity recognition

Can be performed either by local or global approaches

Local approaches Use information contained in the

neighboorhood of pixels

Global approaches Use the entire appearance of the

image

Why a global approach ?

No landmarks have to be detected

No model has to be constructed

Can handle Low resolution Partial occlusions

=> Only the object has to be detected

Existing Global Approaches

PCA, KDA,… [Pentland91] Sensitive to alignment Number of dimensions ?

Neural networks Number of cells in the hidden layer ? Recovery of prototypes of image

classes ?

=> Auto-associative Memories [Abdi94]

Plan of the talk

1) Our approach

2) Results

3) Comparison with other techniques

1. Normalized object imagette Grey scale face imagette normalized in

size and slant: 25x25 pixels

=> Computation time reduction=> Size and slant robustness

1. Auto-associative memories

Linear auto-associative memory

Input patterns associated with themselves

Connection between input unitsPortion of an input =>Complete pattern

X’ = W.X X : Source image X’ : Output image W : Weights

1. Hebbian learning rule

W = Xk.XkT

Faces not well discriminated [Valentin94]

1. Widrow-Hoff learning rule (1)

Learned images are reconstructed

Other images are degraded [Valentin94]

1. Widrow-Hoff learning rule (2)

Creation of prototypes

Eigenvalues egalization [Abdi & Valentin94]

We adapt Widrow-Hoff learning to entity recognition

1. Entity Recognition

Compare the input image to all responses=> Score between 0 and 1

Winner-takes-all process

-> ½ videos for training,½ videos for test

1. Training and Test

Training ->Test V

2. Experiments

3 Experiments :

1) Classes 0 / 1 person Without training a 0 person class

2) Classes 0 / 1 person

3) Classes 0 / 1+ person

2. Result of the first experiment (1)

2. Result of the first experiment (2) Not sufficient for reliable

classification

0 person class imagettes have non-uniform variations in appearance

=> Learn a 0 person class from random images of the background

2. Result of the second experiment

2. Result of the third experiment

2. Recall and precision

Experiment

Classes

11/0

21/0

31+/0

1st class recall

- 99 % 99 %

2nd class recall

- 68 % 70 %

1st class precision

- 95 % 93 %

2nd class precision

- 93 % 90 %

3. Discussion

Training the 0 person class improves discrimination

Some 0 person class images are misclassified :

3. Advantages Varying the size of the imagette do not

have much influence -> 25x25 pixels

Normalization + Classification is done at video-rate

Prototypes can be saved and reused

Can be adapted to entity recognition

Conclusion

+ Invariance to scale, slant and alignment

+ Not disrupted by local changes

- Needs to train a non-person class

Adapted to the project Low resolution Changes of viewpoint Fast