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Transcript of Fei-Fei Li Lecture 12 - 3-Nov- 2016-11-03¢  Fei-Fei Li Lecture 12 - 3-Nov-2016...

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Salvador Dalí “Man/couple with sleeping dog" (1948)

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Slide credit: Steve Seitz, Kristen Grauman

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Slide credit: Svetlana Lazebnik

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Berkeley Segmentation Dataset [Martin et al., ICCV 2001]

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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    “superpixels”

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    http://ttic.uchicago.edu/~xren/research/iccv2003/ http://ttic.uchicago.edu/~xren/research/iccv2003/ https://www.cs.sfu.ca/~mori/research/superpixels/

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    50x50 Patch 50x50 Patch

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    [Felzenszwalb and Huttenlocher 2004]

    [Mori et al. 2005]

    http://cs.brown.edu/~pff/papers/seg-ijcv.pdf https://www.cs.sfu.ca/~mori/research/papers/mori_iccv05.pdf

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    “GrabCut” [Rother et al. 2004]

    https://cvg.ethz.ch/teaching/cvl/2012/grabcut-siggraph04.pdf

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Oversegmentation Undersegmentation

    Multiple Segmentations

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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    … →

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Slide credit: Kristen Grauman

    What things should be grouped?

    What cues indicate groups?

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    • • •

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Slide credit: Kristen Grauman

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Slide credit: Kristen Grauman

    [ ][ ][ ]

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Slide credit: Kristen Grauman

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Slide credit: Kristen Grauman

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    http://www.michaelbach.de/ot/sze-muelue/index.html http://www.michaelbach.de/ot/sze-muelue/index.html

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    [Gregory 1968]

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    [Gregory 1968]

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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    http://en.wikipedia.org/wiki/Gestalt_psychology http://en.wikipedia.org/wiki/Gestalt_psychology

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    • – –

    Untersuchungen zur Lehre von der Gestalt, Psychologische Forschung, Vol. 4, pp. 301-350, 1923

    “I stand at the window and see a house, trees, sky. Theoretically I might say there were 327 brightnesses and nuances of colour. Do I have "327"? No. I have sky, house, and trees.”

    Max Wertheimer (1880-1943)

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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    https://en.wikipedia.org/wiki/Necker_cube https://en.wikipedia.org/wiki/Necker_cube

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    https://en.wikipedia.org/wiki/Rubin_vase

    https://en.wikipedia.org/wiki/Rubin_vase https://en.wikipedia.org/wiki/Rubin_vase https://en.wikipedia.org/wiki/Edgar_Rubin https://en.wikipedia.org/wiki/Edgar_Rubin

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    https://en.wikipedia.org/wiki/Rubin_vase

    Multistability

    https://en.wikipedia.org/wiki/Rubin_vase https://en.wikipedia.org/wiki/Rubin_vase

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    Man and crane, Mimbres culturepot, c. 1000 -1150 AD

    second century B.C. Greek mosaic from the Acropolis

    https://en.wikipedia.org/wiki/Mimbres_culture

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    • • •

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    (or bottom-up hierarchical clustering)

    Simple algorithm

    ● Initialization: ○ Every point is its own cluster

    ● Repeat: ○ Find “most similar” pair of clusters ○ Merge into a parent cluster

    ● Until: ○ The desired number of clusters has been reached ○ There is only one cluster

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    ● Initialization: ○ Every point is its own cluster

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    ● Initialization: ○ Every point is its own cluster

    ● Repeat: ○ Find “most similar” pair of

    clusters

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    ● Initialization: ○ Every point is its own cluster

    ● Repeat: ○ Find “most similar” pair of

    clusters ○ Merge into a parent cluster

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    ● Initialization: ○ Every point is its own cluster

    ● Repeat: ○ Find “most similar” pair of

    clusters ○ Merge into a parent cluster

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    ● Initialization: ○ Every point is its own cluster

    ● Repeat: ○ Find “most similar” pair of

    clusters ○ Merge into a parent cluster

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    - - - -

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  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    ● ○

    http://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html

    http://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html http://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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  • Lecture 12 - 3-Nov-2016Fei-Fei Li

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  • Lecture 12 - 3-Nov-2016Fei-Fei Li

    http://gth.krammerbuch.at/sites/default/files/articles/Create%20Article/Wade_Artistic_Precursors.pdf http://gth.krammerbuch.at/sites/default/files/articles/Create%20Article/Wade_Artistic_Precursors.pdf