CVPR2010: Context-aware saliency detection
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CVPR2010: Context-aware saliency detection

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CVPR2010: Context-aware saliency detection CVPR2010: Context-aware saliency detection Presentation Transcript

  •  
    • Olympic weight lifter
    • Olympic victory
    • Olympic achievement
    • Olympic weight lifter
    • Olympic victory
    • Olympic achievement
    Description based on “salient” pixels only
  • Itti & Koch, 2001 Bruce Tsotsos, 2009 Judd et al., 2009 Previous talk of the session
  • Liu et al, 2007
  • Grab-Cut, Rother et al., 2004
  • Our goal: Convey the image content
  • Stas Goferman Lihi Zelnik-Manor Ayellet Tal
    • Principles for context-aware saliency
    • A saliency detection algorithm
    • Applications:
      • Image retargeting
    • Principles for context-aware saliency
    • A saliency detection algorithm
    • Applications:
      • Image retargeting
      • Collages
    • Following perceptual properties
    • Local low-level factors
      • Contrast
      • Color
    Walther & Koch, 2006
    • Global considerations
      • Maintain unique features
    Hou & Zhang, 2007
    • Local & global
      • Should be multi-scale
    Liu et al, 2007
    • Visual organization (Gestalt)
      • Few centers of gravity
      • Position is important!!
    Our foci
    • High-level
      • Faces
      • Objects
      • People
    Judd et al, 2009 Low-level With face detection
  • Our result
  • Our result Local Walther & Koch, 2006 Global Hou & Zhang, 2007 Local + global Liu et al, 2007
    • The steps of our algorithm
    • Principles 1-2:
    • Unique appearance  salient
    salient Not salient
    • Principles 1-2:
    • Unique appearance  salient
    • Principles 1-2:
    • Unique appearance  salient
    Euclidean distance between colors of patches at p i & p j
    • Principles 1-2:
    • Unique appearance  salient
    salient high
    • Principle 3:
    • Position is important!
    Similar patches both near and far Not salient
    • Principle 3:
    • Position is important!
    Similar patches near Salient
    • Principle 3:
    • Position is important!
    Normalized Euclidean distance between positions of p i & p j
    • Distance between a pair of patches:
    • Distance between a pair of patches:
    salient High
    • Distance between a pair of patches:
    salient High for K most similar
  • K most similar patches at scale r
  •  
    • Salient at:
      • Multiple scales  foreground
      • Few scales  background
    Scale 1 Scale 4
    • Principle 3:
      • Few centers of gravity
    Context
    • Foci =
    • Include distance map
    X
    • Realizing Principles 1,2,3 at multiple scales
    • Principle 4:
      • Faces
      • Objects
    Excluded from this talk
    • Single-scale saliency
    • Multiple scales
    • Final saliency
    X
  • Walther & Koch, 2006 Hou & Zhang, 2007 Our result
  • Walther & Koch, 2006 Hou & Zhang, 2007 Our result
  • Walther & Koch, 2006 Hou & Zhang, 2007 Our result
  • Walther & Koch, 2006 Hou & Zhang, 2007 Our result
  • Walther & Koch, 2006 Hou & Zhang, 2007 Our result
  • Walther & Koch, 2006 Hou & Zhang, 2007 Our result
  • Database of Hou & Zhang
  • Liu et al, 2007 Our result
    • Image retargeting
    • Collage
  • Seam Carving Our result
  • Seam Carving Our result
  • Seam Carving Our result
  •  
  •  
  •  
  •  
    • New definition: Context-aware saliency
    • Algorithm: Based on 4 perceptual principles
    • Applications
    salient Not salient