Unsupervised object discovery via self-organisation Presenter : Bo-Sheng Wang Authors: Teemu Kinnunen, Joni-Kristian Kamarainen, Lasse Lensu, Heikki Kälviäinen PR,

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

Experiments- Caltech-101 vs r-Caktech

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

Experiments- Unsupervised object discovery from r- Caltech

Experiments- Unsupervised object discovery from r- Caltech

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