1 Recognition by Association: ask not “What is it?” ask “What is it like?” Tomasz Malisiewicz and Alyosha Efros CMU CVPR’08.

1

Recognition by Association:ask not “What is it?”

ask “What is it like?”

Tomasz Malisiewicz and Alyosha EfrosCMU

CVPR’08

Understanding an Image

slide by Fei Fei, Fergus & Torralba

Object naming -> Object categorization

sky

building

flag

wallbanner

bus

cars

bus

face

street lamp

slide by Fei Fei, Fergus & Torralba

Object categorization

sky

building

flag

wallbanner

bus

cars

bus

face

street lamp

5

Classical View of Categories

• Dates back to Plato & Aristotle 1. Categories are defined by a

list of properties shared by all elements in a category

2. Category membership is binary

3. Every member in the category is equal

6

Problems with Classical View• Humans don’t do this!

– People don’t rely on abstract definitions / lists of shared properties (Rosch 1973)• e.g. Are curtains furniture?

– Typicality• e.g. Chicken -> bird, but bird -> eagle, pigeon, etc.

– Intransitivity• e.g. car seat is chair, chair is furniture, but …

– Not language-independent• e.g. “Women, Fire, and Dangerous Things” category is

Australian aboriginal language (Lakoff 1987)

–Doesn’t work even in human-defined domains• e.g. Is Pluto a planet?

Problems with Visual CategoriesChair

•A lot of categories are functional

Car

•Different views of same object can be visually dis-similar

8

Categorization in Modern Psychology

• Prototype Theory (Rosch 1973)–One or more summary representations (prototypes) for

each category–Humans compute similarity between input and

prototypes

• Exemplar Theory (Medin & Schaffer 1978, Nosofsky 1986, Krushke 1992)–categories represented in terms of remembered objects

(exemplars)–Similarity is measured between input and all exemplars– think non-parametric density estimation

8

Different way of looking at recognition

CarCarCar

Road

Building

Input Image

10

Different way of looking at recognition

11

What is the ultimate goal?

• Parsing Images

• A “what is it like?” machine

• A kind of “visual memex”

12

Recognition as Association

LabelMe Dataset

12,905 Object Exemplars171 unique ‘labels’

http://labelme.csail.mit.edu/

13

Our Contributions

• Posing Recognition as Association–Use large number of object exemplars

13

•Learning Object Similarity–Different distance function per exemplar

•Recognition-Based Object Segmentation– Use multiple segmentation approach

14

Measuring Similarity

15

Exemplar Representation

Segment from LabelMe

16

ShapeCentered Mask

Bounding Box Dimensions

Pixel Area

Obj~Obj

17

TextureTextons

top,bot,left,right boundary

Interior: Bag-of-Words

18

ColorMean Color

Color std

Color Histogram

19

LocationAbsolute Position Mask

0

1

.42

.8 Top Height

Bot Height

Distance “Similarity” Functions

• Positive Linear Combinations of Elementary Distances Computed Over 14 Features

Building e Distance Function

Building e

Learning Object Similarity

• Learn a different distance function for each exemplar in training set

• Formulation is similar to Frome et al [1,2][1] Andrea Frome, Yoram Singer, Jitendra Malik. "Image Retrieval and Recognition Using Local Distance Functions." In NIPS, 2006.

[2] Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik. "Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification." In ICCV, 2007.

22

Non-parametric density estimation

Color Dimension

Sh

ap

e D

imen

sio

n

Class 1Class 2

Class 3

23

Non-parametric density estimation

Color Dimension

Sh

ap

e D

imen

sio

n

Class 1Class 2

Class 3

24

Non-parametric density estimation

Color Dimension

Sh

ap

e D

imen

sio

n

Class 1Class 2

Class 3

25

Learning Distance Functions

25Dshape

Dcolor

Focal Exemplar

26

Learning Distance Functions

26Dshape

Dcolor

Focal Exemplar

“similar” side

DecisionDecisionBoundaryBoundary

“dissimilar” side

Don’t Care

Visualizing Distance Functions (Training Set)

Query

Query

Top Neighbors with Tex-Hist Dist

Top Neighbors with Learned Dist

Visualizing Distance Functions (Training Set)

Visualizing Distance Functions (Training Set)

Visualizing Distance Functions (Training Set)

personperso

nperso

nperso

nperso

n

Visualizing Distance Functions (Training Set)

3232

Visualizing Distance Functions (Training Set)

personperso

nperso

n

standing

personwoman

person

Different Label on “similar” side of distance function

Labels Crossing Boundary

34

“Conventional” Recognition in Test Set

• Compute the similarity between an input and all exemplars

• All exemplars with D < 1 are “associated” with the input

• Most occurring label from associations is propagated onto input

• Association confidence score favors more associations and smaller distances

34

Performance on labeling perfect segments (test set)

Object Segmentation via Recognition

• Generate Multiple Segmentations (Hoiem 2005, Russell 2006, Malisiewicz 2007)

– Mean-Shift and Normalized Cuts

– Use pairs and triplets of adjacent segments

– Generate about 10,000 segments per image

• Enhance training with bad segments

• Apply learned distance functions to bottom-up segments

37

Example AssociationsBottom-Up Segments

38

Quantitative Evaluation

38

Object hypothesis is correct if labels match and OS > .5

*We do not penalize for multiple correct overlapping associations

OS(A,B) = Overlap Score = intersection(A,B) / union(A,B)

39

Toward Image Parsing

39

40

Conclusion: Main Points• Object Association: defining an object in terms of a set of visually similar objects. Trying to get away from classes.

• Learning per-examplar-distances: each object gets to decide on its own distance function. Suddenly, NN distances are meaningful!

• Using multiple segmentations: partition the input image into manageable chunks than can then be matched

41

Thank You

41

Questions?