Presenter : Bo-Sheng Wang Authors : TeemuKinnunen, Joni-KristianKamarainen, LasseLensu, HeikkiKälviäinen PR, 2012 Unsupervised object discovery via self-organisation
Outlines Motivation Objectives Methodology Experiments Compary Conclusions Comments
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.
Objectives Unsupervised visual object categorisation (UVOC) in which the purpose is to automatically ﬁnd the number of categories in an unlabelled image set.
Methodology- Performance evaluation Sivic et al. (2008)
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.
Methodology-Performance evaluation For the first case： → 1. ‘‘Purity” → 2. Conditional entropy
Conclusions The proposed method achieves accuracy similar to the best method and has some beneﬁcial properties. The self-organisingmap is less sensitive to the success of data normalisation than the k-means algorithm.
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