Deformable Template as Active Basis
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Deformable Template as Active Basis Zhangzhang Si UCLA Department of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song-Chun Zhu ICCV07 PowerPoint PPT Presentation


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Deformable Template as Active Basis Zhangzhang Si UCLA Department of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song-Chun Zhu ICCV07 The work presented in this 2007 talk is outdated, see http://www.stat.ucla.edu/~ywu/AB/ActiveBasisMarkII.html for the most updated results.

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Deformable Template as Active Basis Zhangzhang Si UCLA Department of Statistics Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song-Chun Zhu ICCV07

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Civs statistics dept ucla

Deformable Template as Active Basis

Zhangzhang Si

UCLA Department of Statistics

Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song-Chun Zhu ICCV07

The work presented in this 2007 talk is outdated, see

http://www.stat.ucla.edu/~ywu/AB/ActiveBasisMarkII.html

for the most updated results

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Motivation

Design a deformable template to model a set of images of a certain object category. The template can be learned from example images.

2014/6/6

CIVS, Statistics Dept. UCLA

2


Civs statistics dept ucla

Related work

  • Representation: generative and deformable models

  • Sparse coding [Olshausen-Field 96]

  • Deformable templates [Yuille-Hallinan-Cohen 89]

  • Active contours [Kass-Witkin-Terzopoulos 87]

  • Active appearance[Cootes-Edwards-Taylor 95]

  • Texton model[Zhu et.al. 02]

  • Computation: learning and pursuit algorithm

  • 1. Matching pursuit [Mallat and Zhang 93]

  • 2. HMAX [Riesenhuber-Poggio 99, Mutch-Lowe 06]

  • 3.Adaboost [Freund-Shapire 96, Viola-Jones 99]

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Linear additive image model

Image reconstruction by matching pursuit.

selected from a dictionary of Gabor wavelet elements

location

scale

orientation

  • Two extensions:

  • Encoding a single image Simultaneously encoding a set of images;

  • Allow each Gabor wavelet element Bi to locally perturb.

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The active basis model

(Gabor elements represented by bar)

“Active”: Local perturbation

When encoding image Im, we use

the perturbed version of Bi:

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

An incoming car image:

Deformable template using active basis

A car template

(Gabor elements represented by bar)

2014/6/6

CIVS, Statistics Dept. UCLA

6


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Deformed to fit many car instances

Deformable template using active basis

A car template

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

B1

B2

B3

Learning the template: pursuing the active basis

q(I): background distribution

(all natural images)

p(I): pursued model to approximate

the true distribution.

Example images

# Gabor elements selected

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Pursuing the active basis

MLE:

(Projected on {B1,…,Bn})

(orthogonality of {B1,…,Bn})

2014/6/6

CIVS, Statistics Dept. UCLA

9


Civs statistics dept ucla

Pursuing the active basis

2014/6/6

CIVS, Statistics Dept. UCLA

10


Civs statistics dept ucla

Shared pursuit algorithm

2014/6/6

CIVS, Statistics Dept. UCLA

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Learning the template: pursuing the active basis

A car template consisting of 60 Gabor elements

Car instances

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Experiment 1: learning an active basis model of vehicle

template

  • 37 training images, listed in the descending order of log-likelihood ratio

  • 4.3 seconds (Core 2 Duo 2.4GHz) , after convolution

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Experiment 2: learning without alignment

Active basis pursuit + EM

Given bounding box for the first example for initialization.

Iterate:

- Estimate the bounding boxes using current model.

- Re-learn the model from estimated bounding boxes.

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Learning active basis

EM clustering

Experiment 3: learning and clustering

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Civs statistics dept ucla

Experiment 4: car detection with active basis model

  • Scan bounding box over the image at multi-resolutions

  • Compute log-likelihood ratio by combining responses from active basis

LLR: log likelihood ratio

LLR: log likelihood ratio

Maximum LLR over scale

map of LLR at optimal scale

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Civs statistics dept ucla

Experiment 5: head-and-shoulder recognition

Features: using the same set of Gabor filters.

Some negatives

Some positives

Negatives include various in-door and out door scenes, with and without human

Human head andshoulders, roughly aligned

43 training positives, 157 training negatives

88 testing positives, 474 testing negatives

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Experiment 5: head-and-shoulder recognition

comparing with Adaboost

ROC of sigmoid model is a further improvement of the result presented in the paper.

CIVS, Statistics Dept. UCLA


Civs statistics dept ucla

Main contributions

1. An active basis model as deformable template.

2. A shared pursuit algorithm for fast learning.

http://www.stat.ucla.edu/~ywu/ActiveBasis.html

Download

1) Training and testing images

2) Matlab and mex-C source codes that reproduce all the experiments in the paper and powepoint.

CIVS, Statistics Dept. UCLA


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