Extracting Adaptive Contextual Cues From Unlabeled Regions

Congcong Li+, Devi Parikh*, Tsuhan Chen+

+ Cornell University* Toyota Technological Institute at Chicago

International Conference on Computer Vision 2011Poster ID:

2-46

Object Detection with Context

plantchair

sofa

plant

Previous: Focus on labeled objects but neglect unlabeled regions

Labeled vs Unlabeled

55% 45%72%28%

MSRC dataset PASCAL 07 dataset

Human Study: unlabeled regions help

Is unlabeled region useful?

Our View: Leverage unlabeled regions

plant

‘plant’ context

Our view: Extract adaptive context

Inter-object Intra-objectScene

• Ours: Context at adaptive granularities Multi-level Interactions!

• Prior works: Context at fixed granularity

20%

EXO: expand fixed ratio Scene: whole image

Contextual-Meta Objects (CMO)

Algorithm: discovering contextual regions

Database

Extent-based Clustering

Content-based Clustering

... …

Learn “object” Models... …

Context Detector

Results on PASCAL 2007

31.5

32

32.5

33

33.5

34

34.5

Scene EXO CMO

Adaptive granularity helps!

32.5

33

33.5

34

34.5

35

35.5

Labeled Unlabeled Combined

Unlabeled: complementary context

fixedgranularity

adaptive

Results: improve multiple detectors!

22

23

24

25

26

27

28

24

26.8

Labeled objects Unlabeled regions

Dalal-Triggs10

11

12

13

14

15

16

12.6

15.6

Implicit Shape Model30

31

32

33

34

35

36

32.3

34.2

Part-based Model

Can employ any object detector to learn the contextual “object”！

Results: provide spatial prior for OOI

Contributions

• Extracting contextual cues from unlabeled regions

• Capturing contextual interactions at varying levels: Scene, Inter-object, Intra-object

• Extracting contextual regions by learning “object” models using any object detector

• Intelligently leveraging existing techniques: easily accessible to community

Thank you!

International Conference on Computer Vision 2011Poster ID:

2-46

Visit our project page: http://chenlab.ece.cornell.edu/projects/AdaptiveContext/