1. Stas Goferman Lihi Zelnik-Manor Ayellet Tal

2. …

4.  Man in a flower field  In the fields  Spring blossom

6.  Olympic weight lifter  Olympic victory  Olympic achievement

7.  Man in a flower field  In the fields  Spring blossom  Olympic weight lifter  Olympic victory  Olympic achievement

8.  Man in a flower field  In the fields  Spring blossom  Olympic weight lifter  Olympic victory  Olympic achievement

9. Following perceptual properties

10.  Local low-level factors Contrast Color

11. [Walther and Koch, Neural Networks 2006]

12.  Local low-level factors Contrast Color Walther & Koch, 2006

13.  Global considerations Maintain unique features

14. [Hou & Zhang CVPR 2007]

15.  Global considerations Maintain unique features Hou & Zhang, 2007

16.  Local & global

17. InputMulti-scale contrast Center surround ColorFinal [Liu et al, CVPR 2007]

18.  Local & global Liu et al, 2007

19.  Visual organization (Gestalt) Few centers of gravity [Koffka] Position is important!!

20.  High-level Faces Objects People … [Judd et al, ICCV 2009] Low-level With face detection

21. Our result

22. Local Walther & Koch, 2006 Global Hou & Zhang, 2007 Local + global Liu et al, 2007

23. The steps of our algorithm

24.  Principles 1-2: Unique appearance  salient salient Not salient

25.  Principles 1-2: Unique appearance  salient

26.  Principles 1-2: Unique appearance  salient Euclidean distance between colors of patches at p i & p j

27.  Principles 1-2: Unique appearance  salient high salient

28.  Principle 3: Position is important! Similar patches both near and far Not salient

29.  Principle 3: Position is important! Similar patches near Salient

30.  Principle 3: Position is important! Normalized Euclidean distance between positions of p i & p j

31.  Distance between a pair of patches: salient High

32.  Distance between a pair of patches: salient High for K most similar

33. K most similar patches at scale r

35.  Salient at: Multiple scales  foreground Few scales  background Scale 1Scale 4

36.  Principle 3: Few centers of gravity Context

37. X Final result Focus points Distance map

38.  Single-scale saliency  Multiple scales  Final saliency X

39. …

40. Walther & Koch, 2006Hou & Zhang, 2007 Our result

41. Walther & Koch, 2006Hou & Zhang, 2007 Our result

42. Walther & Koch, 2006Hou & Zhang, 2007 Our result

43. Walther & Koch, 2006Hou & Zhang, 2007 Our result

44. Walther & Koch, 2006Hou & Zhang, 2007 Our result

45. Walther & Koch, 2006Hou & Zhang, 2007 Our result

46. Database of Hou & Zhang

47. Our Our + center Judd

48. Our Our + center Judd

50. InputOur resultBoiman & Irani

51. InputOur resultBoiman & Irani

52. Image resizing

53. Liu et al, 2007 Our result

54. Seam CarvingOur result Liu et al [Avidan et al, SIGGRPH’07]

55. Seam CarvingOur result [Avidan et al, SIGGRPH’07]

56. Seam CarvingOur result [Avidan et al, SIGGRPH’07]

57. Combines local & global saliency Incorporates perceptual considerations State-of-the-art results Code is available

58. Long run-time (~30 sec for 250x250 pixels) Repetitive texture is totally eliminated Can we control how much context is included?

59. Can it be extended to video? Is there a faster implementation

60. …