Object Recognition by Discriminative Methods Sinisa Todorovic 1st Sino-USA Summer School in VLPR July, 2009 - PowerPoint PPT Presentation

About This Presentation
Title:

Object Recognition by Discriminative Methods Sinisa Todorovic 1st Sino-USA Summer School in VLPR July, 2009

Description:

... only in the vector product No need to compute the vector product in the new space Mercer kernels Vision Applications Pedestrian detection multiscale scanning ... – PowerPoint PPT presentation

Number of Views:24
Avg rating:3.0/5.0
Slides: 46
Provided by: yao1
Learn more at: https://cs.stanford.edu
Category:

less

Transcript and Presenter's Notes

Title: Object Recognition by Discriminative Methods Sinisa Todorovic 1st Sino-USA Summer School in VLPR July, 2009


1
Object Recognition by Discriminative
MethodsSinisa Todorovic1st Sino-USA Summer
School in VLPRJuly, 2009
2
Motivation
Discriminate yes, but what?
Problem 1 What are good features
from Kristen Grauman, B. Leibe
3
Motivation
Discriminate yes, but against what?
Problem 2 What are good training examples
from Kristen Grauman, B. Leibe
4
Motivation
Discriminate yes, but how?
Problem 3 What is a good classifier
from Kristen Grauman, B. Leibe
5
Motivation
Sliding windows????
What is a non-car?
Is Bayes decision optimal?
from Kristen Grauman, B. Leibe
6
How to Classify?
observable feature
class
discriminant function
Discriminant function may have a probabilistic
interpretation
7
How to Classify?
Generative
From Antonio Torralba 2007
8
How to Classify?
Discriminative
From Antonio Torralba 2007
9
How to Classify?
observable feature
class
discriminant function
Linear discriminant function
10
How to Classify?
Var 2
margin width that the boundary can be
increased before hitting a datapoint
margin 1
margin 2
Var 1
Large margin classifiers
11
Distance Based Classifiers
Given
query
12
Outline
Nearest Neighbor
Shakhnarovich, Viola, Darrell, Torralba, Efros,
Berg, Frome, Malik, Todorovic...
13
Maxim-Margin Linear Classifier
support vectors
14
Maxim-Margin Linear Classifier
Problem
subject to
15
Maxim-Margin Linear Classifier
After rescaling data
Problem
subject to
16
LSVM Derivation
17
LSVM Derivation
Problem
s.t.
18
Dual Problem
Solve using Lagrangian
19
Dual Problem
s.t.
20
Linearly Non-Separable Case
trade-off between maximum separation and
misclassification
s.t.
21
Non-Linear SVMs
  • Non-linear separation by mapping data to another
    space
  • In SVM formulation, data appear only in the
    vector product
  • No need to compute the vector product in the new
    space
  • Mercer kernels

22
Vision Applications
  • Pedestrian detection
  • multiscale scanning windows
  • for each window compute the wavelet transform
  • classify the window using SVM

A general framework for object detection, C.
Papageorgiou, M. Oren and T. Poggio -- CVPR 98
23
Vision Applications
A general framework for object detection, C.
Papageorgiou, M. Oren and T. Poggio -- CVPR 98
24
Shortcomings of SVMs
  • Kernelized SVM requires evaluating the kernel for
    a test vector and each of the support vectors
  • Complexity Kernel complexity number of
    support vectors
  • For a class of kernels this can be done more
    efficiently Maji,Berg, Malik CVPR 08

intersection kernel
25
Outline
26
Distance Based Classifiers
Given
query
27
Learning Global Distance Metric
  • Given query and datapoint-class pairs
  • Learn a Mahalanobis distance metric that
  • brings points from the same class closer, and
  • makes points from different classes be far away

Distance metric learning with application to
clustering with side information E. Xing, A. Ng,
and M. Jordan, NIPS, 2003.
28
Learning Global Distance Metric
s.t.
PROBLEM!
29
Learning Global Distance Metric
Problem with multimodal classes
before learning
after learning
30
Per-Exemplar Distance Learning
Frome Malik ICCV07, Todorovic Ahuja CVPR08
31
Distance Between Two Images
distance between j-th patch in image F and image I
32
Learning from Triplets
For each image I in the set we have
33
Max-Margin Formulation
  • Learn for each focal image F independently

s.t.
34
Max-Margin Formulation
distance between j-th patch in image F and image I
35
EM-based Max-Margin Formulation of Local
Distance Learning
TodorovicAhuja CVPR08
36
Learning from Triplets
For each image I in the set
Frome, Malik ICCV07
Todorovic et al. CVPR08
37
CVPR 2008 Results on Caltech-256
38
Outline
39
Decision Trees -- Not Stable
  • Partitions of data via recursive splitting on a
    single feature
  • Result Histograms based on data-dependent
    partitioning
  • Majority voting
  • Quinlan C4.5 Breiman, Freedman, Olshen, Stone
    (1984) Devroye, Gyorfi, Lugosi (1996)

40
Random Forests (RF)
RF Set of decision trees such that
each tree depends on a random vector
sampled independently and with the same
distribution for all trees in RF
41
Hough Forests
Combine spatial info class info
Class-Specific Hough Forests for Object
Detection Juergen Gall and Victor Lempitsky CVPR
2009
42
Hough Forests
43
Hough Forests
In the test image all features cast votes about
the location of the bounding box
Class-Specific Hough Forests for Object
Detection Juergen Gall and Victor Lempitsky CVPR
2009
44
Hough Forests
Class-Specific Hough Forests for Object
Detection Juergen Gall and Victor Lempitsky CVPR
2009
45
Thank you!
Write a Comment
User Comments (0)
About PowerShow.com