60
Context-aware saliency Stas Goferman Lihi Zelnik-Manor Ayellet Tal

Stas Goferman Lihi Zelnik-Manor Ayellet Tal. …

  • View
    217

  • Download
    3

Embed Size (px)

Citation preview

  • Slide 1
  • Stas Goferman Lihi Zelnik-Manor Ayellet Tal
  • Slide 2
  • Slide 3
  • Slide 4
  • Man in a flower field In the fields Spring blossom
  • Slide 5
  • Slide 6
  • Olympic weight lifter Olympic victory Olympic achievement
  • Slide 7
  • Man in a flower field In the fields Spring blossom Olympic weight lifter Olympic victory Olympic achievement
  • Slide 8
  • Man in a flower field In the fields Spring blossom Olympic weight lifter Olympic victory Olympic achievement
  • Slide 9
  • Following perceptual properties
  • Slide 10
  • Local low-level factors Contrast Color
  • Slide 11
  • [Walther and Koch, Neural Networks 2006]
  • Slide 12
  • Local low-level factors Contrast Color Walther & Koch, 2006
  • Slide 13
  • Global considerations Maintain unique features
  • Slide 14
  • [Hou & Zhang CVPR 2007]
  • Slide 15
  • Global considerations Maintain unique features Hou & Zhang, 2007
  • Slide 16
  • Local & global
  • Slide 17
  • InputMulti-scale contrast Center surround ColorFinal [Liu et al, CVPR 2007]
  • Slide 18
  • Local & global Liu et al, 2007
  • Slide 19
  • Visual organization (Gestalt) Few centers of gravity [Koffka] Position is important!!
  • Slide 20
  • High-level Faces Objects People [Judd et al, ICCV 2009] Low-level With face detection
  • Slide 21
  • Our result
  • Slide 22
  • Local Walther & Koch, 2006 Global Hou & Zhang, 2007 Local + global Liu et al, 2007
  • Slide 23
  • The steps of our algorithm
  • Slide 24
  • Principles 1-2: Unique appearance salient salient Not salient
  • Slide 25
  • Principles 1-2: Unique appearance salient
  • Slide 26
  • Principles 1-2: Unique appearance salient Euclidean distance between colors of patches at p i & p j
  • Slide 27
  • Principles 1-2: Unique appearance salient high salient
  • Slide 28
  • Principle 3: Position is important! Similar patches both near and far Not salient
  • Slide 29
  • Principle 3: Position is important! Similar patches near Salient
  • Slide 30
  • Principle 3: Position is important! Normalized Euclidean distance between positions of p i & p j
  • Slide 31
  • Distance between a pair of patches: salient High
  • Slide 32
  • Distance between a pair of patches: salient High for K most similar
  • Slide 33
  • K most similar patches at scale r
  • Slide 34
  • Slide 35
  • Salient at: Multiple scales foreground Few scales background Scale 1Scale 4
  • Slide 36
  • Principle 3: Few centers of gravity Context
  • Slide 37
  • X Final result Focus points Distance map
  • Slide 38
  • Single-scale saliency Multiple scales Final saliency X
  • Slide 39
  • Slide 40
  • Walther & Koch, 2006Hou & Zhang, 2007 Our result
  • Slide 41
  • Walther & Koch, 2006Hou & Zhang, 2007 Our result
  • Slide 42
  • Walther & Koch, 2006Hou & Zhang, 2007 Our result
  • Slide 43
  • Walther & Koch, 2006Hou & Zhang, 2007 Our result
  • Slide 44
  • Walther & Koch, 2006Hou & Zhang, 2007 Our result
  • Slide 45
  • Walther & Koch, 2006Hou & Zhang, 2007 Our result
  • Slide 46
  • Database of Hou & Zhang
  • Slide 47
  • Our Our + center Judd
  • Slide 48
  • Our Our + center Judd
  • Slide 49
  • Slide 50
  • InputOur resultBoiman & Irani
  • Slide 51
  • InputOur resultBoiman & Irani
  • Slide 52
  • Image resizing
  • Slide 53
  • Liu et al, 2007 Our result
  • Slide 54
  • Seam CarvingOur result Liu et al [Avidan et al, SIGGRPH07]
  • Slide 55
  • Seam CarvingOur result [Avidan et al, SIGGRPH07]
  • Slide 56
  • Seam CarvingOur result [Avidan et al, SIGGRPH07]
  • Slide 57
  • Combines local & global saliency Incorporates perceptual considerations State-of-the-art results Code is available
  • Slide 58
  • Long run-time (~30 sec for 250x250 pixels) Repetitive texture is totally eliminated Can we control how much context is included?
  • Slide 59
  • Can it be extended to video? Is there a faster implementation
  • Slide 60