Docsity
Docsity

Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity


Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan


Guidelines and tips
Guidelines and tips

Bag of Words-Introduction to Computer Vision-Lecture 15-Computer Science, Lecture notes of Computer Vision

Bag-of-words, Spatial Information, Spatial Pyramid Matching, Implicit Shape Model, Probabilistic Part-Based Model, Generative Part-Based Models, Pictorial Structure Model, Sparse Representation, Greg Shakhnarovich, Lecture Slides, Introduction to Computer Vision, Computer Science, Toyota Technological Institute at Chicago, United States of America.

Typology: Lecture notes

2011/2012

Uploaded on 03/12/2012

alfred67
alfred67 🇺🇸

4.9

(20)

328 documents

1 / 30

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
IntrotoComputerVision
Lecture15
DeviParikh
Research Assistant Professor, TTIC
Research
Assistant
Professor,
TTIC
May20,2010
pf3
pf4
pf5
pf8
pf9
pfa
pfd
pfe
pff
pf12
pf13
pf14
pf15
pf16
pf17
pf18
pf19
pf1a
pf1b
pf1c
pf1d
pf1e

Partial preview of the text

Download Bag of Words-Introduction to Computer Vision-Lecture 15-Computer Science and more Lecture notes Computer Vision in PDF only on Docsity!

Intro

to

Computer

Vision

Lecture

Devi

Parikh

Research Assistant Professor, TTICResearch

Assistant

Professor,

TTIC

May

Last timeLast

time

•^ Given a set of training imagesGiven

a^ set

of^

training

images

•^ Can

classify

a^ new

test

image

d^

i

•^ Two

descriptors:

-^ Bag

‐of‐

words

(objects)

-^ Gist

(scenes)

Adding spatial informationAdding

spatial

information

  • Spatial

Pyramid

Matching:

Spat a

y a

d

atc

g:

  • Computing

bags

of^ features

on^

sub

‐windows

of^ the

whole

image

  • Implicit

Shape

Model:

  • Using

features

(vocabulary)

to^ vote

for^

object

position

  • Probabilistic

Part

‐based

Model:

  • Explicit

shape

mode Many^ slides

adapted

from^ Svetlana

Lazebnik,

Fei‐Fei Li,

Rob^ Fergus,

and^ Antonio

Torralba

Spatial Pyramid MatchingSpatial

Pyramid

Matching

Spatial Pyramid MatchingSpatial

Pyramid

Matching

Spatial Pyramid MatchingSpatial

Pyramid

Matching

Spatial Pyramid MatchingSpatial

Pyramid

Matching

Spatial Pyramid MatchingSpatial

Pyramid

Matching

Implicit Shape ModelImplicit

Shape

Model

Implicit shape modelsImplicit

shape

models

•^ Visual codebook is used to index votes for•^ Visual

codebook

is^ used

to^

index

votes

for

object

position

visual

codeword

with

displacement vectors

training

image

annotated

with^

object

localization

info

displacement

vectors

Implicit shape models: DetailsImplicit

shape

models:

Details

Probabilistic Part

‐based Model

Probabilistic

Part

based

Model

Probabilistic

model

max

objecth p objecth shapep objecth

appearanceP

object shape

appearanceP

object imageP

h

= h: assignment

of^ features

to^ parts

Partdescriptors

Partlocations

Candidate

parts

Probabilistic

model

max

objecth p objecth shapep objecth

appearanceP

object shape

appearanceP

object imageP

h

= h: assignment

of^ features

to^ parts^ Part

1

Part 3Part^3

Part^2