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Unsupervised Object Discovery via Self-Organisation: A Bag-of-Features Approach

This paper presents a method for Unsupervised Visual Object Categorisation (UVOC) using self-organisation and bag-of-features techniques. The approach aims to automatically identify the number of object categories in unlabeled image sets. Experiments show comparable accuracy to state-of-the-art methods with the added advantage of reduced sensitivity to data normalization, making it beneficial for object discovery tasks.

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Unsupervised Object Discovery via Self-Organisation: A Bag-of-Features Approach

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  1. Presenter : Bo-Sheng Wang Authors : TeemuKinnunen, Joni-KristianKamarainen, LasseLensu, HeikkiKälviäinen PR, 2012 Unsupervised object discovery via self-organisation

  2. Outlines Motivation Objectives Methodology Experiments Compary Conclusions Comments

  3. 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.

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

  5. Methodology- Bag-of-Features

  6. Methodology- Self-organisation model

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

  8. 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.

  9. Methodology-Performance evaluation For the first case： → 1. ‘‘Purity” → 2. Conditional entropy

  10. Descriptors

  11. Descriptors-Methodology

  12. Descriptors-Performance

  13. Experiments-Caltech-101 vs r-Caktech-101

  14. Experiments-Caltech-101 vs r-Caktech-101

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

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

  17. Experiments-Unsupervised object discovery from r-Caltech-101

  18. Experiments-Unsupervised object discovery from r-Caltech-101

  19. Experiments-Unsupervised object discovery from r-Caltech-101

  20. 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.

  21. 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

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