unsupervised object discovery via self- organisation
DESCRIPTION
Presenter : Bo- Sheng Wang Authors: Teemu Kinnunen , Joni- Kristian Kamarainen , Lasse Lensu , Heikki Kälviäinen PR, 2012. Unsupervised object discovery via self- organisation. Outlines. Motivation Objectives Methodology Experiments Compary Conclusions Comments. Motivation. - PowerPoint PPT PresentationTRANSCRIPT
Unsupervised object discovery via self-organisation
Presenter : Bo-Sheng Wang Authors : Teemu Kinnunen, Joni-Kristian Kamarainen, Lasse Lensu, Heikki Kälviäinen
PR, 2012
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Outlines
• Motivation• Objectives• Methodology• Experiments• Compary• Conclusions• Comments
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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.
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Objectives• Unsupervised visual object categorisation (UVOC) in
which the purpose is to automatically find the number of categories in an unlabelled image set.
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Methodology- Bag-of-Features
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Methodology- Self-organisation model
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Methodology- Performance evaluation• Sivic et al. (2008)
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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.
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Methodology-Performance evaluation
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• For the first case：→ 1. ‘‘Purity”
→ 2. Conditional entropy
Descriptors
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Descriptors-Methodology
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Descriptors-Performance
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Experiments-Caltech-101 vs r-Caktech-101
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Experiments-Caltech-101 vs r-Caktech-101
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Experiments-Comparison to the state-of-the-art
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Experiments-Comparison to the state-of-the-art
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Experiments-Unsupervised object discovery from r-Caltech-101
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Experiments-Unsupervised object discovery from r-Caltech-101
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Experiments-Unsupervised object discovery from r-Caltech-101
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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.
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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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