Big Visual Data

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Way more efficient approaches: [Ramanan et al 2012, Durand et al 2012]
SEARCH USING PAINTINGS Input Painting Our Approach GIST Bag-of-Words Tiny Images HOG
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What makes Big Visual Data hard? ©
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Driven by Visual Data Texture Synth
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Texture Synthesis
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Throwing away too much too soon? in
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[Efros & Leung, ’99, Efros & Free
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input image Portilla & Simoncelli X
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Lots of data available
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The A.I. for the postmodern world
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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 67 and 68: Input Query Top Matches
Page 69 and 70: IMPORTANT PARTS? Input Query Import
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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