unsupervised object discovery via self-organisation presenter : bo-sheng wang authors: teemu...

Unsupervised object discovery via self-organisation

Presenter : Bo-Sheng Wang 　Authors :

Teemu Kinnunen, Joni-Kristian Kamarainen, Lasse Lensu,

Heikki Kälviäinen

PR, 2012

1

Outlines

• Motivation• Objectives• Methodology• Experiments• Compary• Conclusions• Comments

2

Motivation

• VOC are based on discriminative machine learning and require a large amount of training data that need to be labelled and often also annotated by bounding boxes, landmarks, or object boundaries.

• The baseline problem much worse than for the supervised VOC problem.

3

Objectives

• Unsupervised visual object categorisation (UVOC) in which the purpose is to automatically find the number of categories in an unlabelled image set.

4

Methodology- Bag-of-Features

5

Methodology- Self-organisation model

6

Methodology- Performance evaluation• Sivic et al. (2008)

7

Methodology- Performance evaluation• Tuytelaars et al. (2010)

→ The number of categories is enforced to correspond to the number of ground truth categories

→ The number of produced categories does not correspond to the number of categories in the original data.

8

Methodology-Performance evaluation

9

• For the first case：→ 1. ‘‘Purity”

→ 2. Conditional entropy

Descriptors

10

Descriptors-Methodology

11

Descriptors-Performance

12

Experiments-Caltech-101 vs r-Caktech-101

13

Experiments-Caltech-101 vs r-Caktech-101

14

Experiments-Comparison to the state-of-the-art

15

Experiments-Comparison to the state-of-the-art

16

Experiments-Unsupervised object discovery from r-Caltech-101

17

Experiments-Unsupervised object discovery from r-Caltech-101

18

Experiments-Unsupervised object discovery from r-Caltech-101

19

Conclusions

• The proposed method achieves accuracy similar to the best method and has some beneficial properties.

• The self-organising map is less sensitive to the success of data normalisation than the k-means algorithm.

20

Comments

• Advantages– This paper gives rich experiments for this method– In unsupervised case, find the number of

categories can be save some time.

• Applications– Object Discovery

21