clustering and classification by optimum path forest
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Clustering and Classification by Optimum-Path Forest Alexandre Falc - PowerPoint PPT Presentation

Nov 06, 2023 •142 likes •946 views

Clustering and Classification by Optimum-Path Forest Alexandre Falc ao Institute of Computing - University of Campinas afalcao@ic.unicamp.br Alexandre Falc ao MC920/MO443 - Indrodu c ao ao Proc. de Imagens Introduction New

  1. Clustering and Classification by Optimum-Path Forest Alexandre Falc˜ ao Institute of Computing - University of Campinas afalcao@ic.unicamp.br Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  2. Introduction New technologies for data acquisition and storage have provided large datasets with millions (or more) of samples for statistical analysis. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  3. Introduction New technologies for data acquisition and storage have provided large datasets with millions (or more) of samples for statistical analysis. We need more efficient and effective pattern recognition methods for large datasets. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  4. Introduction New technologies for data acquisition and storage have provided large datasets with millions (or more) of samples for statistical analysis. We need more efficient and effective pattern recognition methods for large datasets. The applications are in many fields of the sciences and engineering. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  5. Introduction New technologies for data acquisition and storage have provided large datasets with millions (or more) of samples for statistical analysis. We need more efficient and effective pattern recognition methods for large datasets. The applications are in many fields of the sciences and engineering. Our main focus has been on image analysis. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  6. Introduction Each sample s (spel, image or object) of a dataset Z can be interpreted as a point of a distance space defined by a simple or composite descriptor. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  7. Introduction Each sample s (spel, image or object) of a dataset Z can be interpreted as a point of a distance space defined by a simple or composite descriptor. We wish to design a classifier which can assign the correct label for any sample s ∈ Z . Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  8. Introduction Each sample s (spel, image or object) of a dataset Z can be interpreted as a point of a distance space defined by a simple or composite descriptor. We wish to design a classifier which can assign the correct label for any sample s ∈ Z . In supervised learning, a labeled set T ⊂ Z is available to train the classifier. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  9. Introduction Each sample s (spel, image or object) of a dataset Z can be interpreted as a point of a distance space defined by a simple or composite descriptor. We wish to design a classifier which can assign the correct label for any sample s ∈ Z . In supervised learning, a labeled set T ⊂ Z is available to train the classifier. In unsupervised learning, there is no knowledge about the labels in T . Clusters can be found and class labels may be assigned to them based on some prior knowledge. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  10. Introduction Some common mistakes are to assume that Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  11. Introduction Some common mistakes are to assume that the classes/clusters form compact clouds of points in the distance space. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  12. Introduction Some common mistakes are to assume that the classes/clusters form compact clouds of points in the distance space. they do not overlap each other. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  13. Introduction Some common mistakes are to assume that the classes/clusters form compact clouds of points in the distance space. they do not overlap each other. one cluster corresponds to one class. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  14. Introduction Some common mistakes are to assume that the classes/clusters form compact clouds of points in the distance space. they do not overlap each other. one cluster corresponds to one class. the probability density function of the classes/clusters present known shapes for parametric modeling. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  15. Introduction We assume that two samples in a same cluster/class should be at least connected by a chain of nearby samples (transitive property). Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  16. Introduction We assume that two samples in a same cluster/class should be at least connected by a chain of nearby samples (transitive property). A graph ( T , A ) is defined by an adjacency relation A between training samples using the distance space. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  17. Introduction We assume that two samples in a same cluster/class should be at least connected by a chain of nearby samples (transitive property). A graph ( T , A ) is defined by an adjacency relation A between training samples using the distance space. A connectivity function f ( π t ) assigns a value to any path π t from its root R ( π t ) to its terminal node t . Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  18. Introduction We assume that two samples in a same cluster/class should be at least connected by a chain of nearby samples (transitive property). A graph ( T , A ) is defined by an adjacency relation A between training samples using the distance space. A connectivity function f ( π t ) assigns a value to any path π t from its root R ( π t ) to its terminal node t . The minimization (maximization) of the connectivity map V ( s ) = ∀ t ∈ Π( T , A , t ) { f ( π t ) } min produces an optimum-path forest rooted at nodes called prototypes. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  19. Introduction In supervised learning, each class is an optimum-path forest rooted at its prototypes, which propagate the class label to the remaining nodes of the forest. class A class A class B Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  20. Introduction In unsupervised learning, each cluster is an optimum-path tree rooted at some prototype, which propagates a cluster label to the remaining nodes of the tree. cluster C cluster A cluster D cluster B Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  21. Introduction This methodology does not assume known shapes, non-overlapping classes, or parametric models. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  22. Introduction This methodology does not assume known shapes, non-overlapping classes, or parametric models. Both learning approaches are fast and robust for training sets of reasonable sizes. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  23. Introduction This methodology does not assume known shapes, non-overlapping classes, or parametric models. Both learning approaches are fast and robust for training sets of reasonable sizes. Label propagation to new samples t ∈ Z\T is efficiently performed based on a local processing of the forest’s attributes and distances between nodes s ∈ T and t . Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  24. Organization of this lecture Supervised classification by OPF [1]. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  25. Organization of this lecture Supervised classification by OPF [1]. Its application to image retrieval [2]. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  26. Organization of this lecture Supervised classification by OPF [1]. Its application to image retrieval [2]. Clustering by OPF [3]. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  27. Organization of this lecture Supervised classification by OPF [1]. CSF Its application to image retrieval [2]. Clustering by OPF [3]. Its application to 3D brain tissue segmentation [4]. WM GM Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  28. Supervised classification Dataset Consider samples from two classes of a dataset. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  29. Supervised classification Training Consider samples from two classes of a dataset. A training set (filled bullets) may not represent data distribution. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  30. Supervised classification 1NN classification Consider samples from two classes of a dataset. A training set (filled bullets) may not represent data distribution. Classification by nearest neighbor fails, when training samples are close to test samples (empty bullets) from other classes. Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

  31. Supervised learning OPF training We can create an optimum-path forest, where V ( s ) is penalized when s is not closely connected to its class. s Alexandre Falc˜ ao MC920/MO443 - Indrodu¸ c˜ ao ao Proc. de Imagens

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