Big Visual Data

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Input Query Top Matches
Input Query Top Matches
Page 1 and 2:
What makes Big Visual Data hard? ©
Page 3 and 4:
Driven by Visual Data Texture Synth
Page 5 and 6:
Texture Synthesis
Page 7 and 8:
Throwing away too much too soon? in
Page 9 and 10:
[Efros & Leung, ’99, Efros & Free
Page 11 and 12:
input image Portilla & Simoncelli X
Page 13 and 14:
Lots of data available
Page 15 and 16: The A.I. for the postmodern world
Page 18 and 19: 140 billion images 6 billion added
Page 20 and 21: Bad News Visual Data is difficult t
Page 22: How similar are two pictures? ? =
Page 27 and 28: INDEXING VIA “VISUAL WORDS”
Page 29 and 30: [SIFT: Lowe, 2004] letter “VISUAL
Page 36 and 37: Visual “Garbage Heap” “It irr
Page 38 and 39: Improving Visual Correspondence
Page 40 and 41: Lots Of Images A. Torralba, R. Ferg
Page 42 and 43: Lots Of Images
Page 44: [Hays & Efros, SIGGRAPH’07]
Page 47 and 48: Scene Descriptor Scene Gist Descrip
Page 58: … 200 scene matches
Page 62 and 63: Improving Visual Correspondence
Page 64: LEARNING BETTER VISUAL CORRESPONDEN
Page 69 and 70: IMPORTANT PARTS? Input Query Import
Page 72 and 73: Way more efficient approaches: [Ram
Page 74 and 75: SEARCH USING PAINTINGS Input Painti
Page 76 and 77: SEARCH USING SKETCHES Input Sketch
Page 78 and 79: APPLICATIONS
Page 80 and 81: Historical Image of Boston Station
Page 82 and 83: INTERNET RE-PHOTOGRAPHY Historical
Page 84 and 85: Input Painting PAINTING2GPS Retriev
Page 86 and 87: VISUAL SCENE EXPLORATION
Page 88 and 89: Query image FINDING SIMILAR IMAGES
Page 90: TRAVERSING THE GRAPH
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Big Visual Data

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