General Principal. Huge Dataset. Images. Info from Most Similar Images. Input Image. Associated Info
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- Winifred Park
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Transcription
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3 Devin Montes
4 General Principal Huge Dataset Input Image Images Associated Info image matching Info from Most Similar Images Hopefully, If you have enough images, the dataset will contain very similar images that you can find with simple matching methods.
5 200 total
6 Graph cut + Poisson blending
7 How much can an image tell about its geographic location?
8 How much can an image tell about its geographic location? 6 million geo-tagged Flickr images im2gps (Hays & Efros, CVPR 2008)
9 Nearest Neighbors according to gist + bag of SIFT + color histogram + a few others
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11 Im2gps
12 Example Scene Matches
13 Voting Scheme
14 im2gps
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17 Effect of Dataset Size
18 Where is This? [Vesselova, Kalogerakis, Hertzmann, Hays, Efros. Image Sequence Geolocation. ICCV 09]
19 Where is This?
20 Where are These? 15:14, June 18 th, :31, June 18 th, 2006
21 Where are These? 15:14, June 18 th, :31, June 18 th, :24, June 19 th, 2006
22 Results im2gps 10% (geo-loc within 400 km) temporal im2gps 56%
23 Tiny Images 80 million tiny images: a large dataset for nonparametric object and scene recognition Antonio Torralba, Rob Fergus and William T. Freeman. PAMI
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28 Human Scene Recognition
29 Humans vs. Computers: Car-Image Classification Humans for 32 pixel tall images Various computer vision algorithms for full resolution images
30 Powers of 10 Number of images on my hard drive: 10 6 Number of images seen during my first 10 years: 10 8 (3 images/second * 60 * 60 * 16 * 365 * 10 = 630,720,000) Number of images seen by all humanity: ,456,367,669 humans 1 * 60 years * 3 images/second * 60 * 60 * 16 * 365 = 1 from Number of photons in the universe: Number of all 32x32 images: *32*3 ~
31 Understanding scenes encompasses all kinds of knowledge
32 But not all scenes are so original
33 Lots Of Images A. Torralba, R. Fergus, W.T.Freeman. PAMI 2008
34 Lots Of Images A. Torralba, R. Fergus, W.T.Freeman. PAMI 2008
35 Lots Of Images
36 Application: Automatic Colorization Input Color Transfer Color Transfer Matches (gray) Matches (w/ color) Avg Color of Match
37 Application: Automatic Colorization Input Color Transfer Color Transfer Matches (gray) Matches (w/ color) Avg Color of Match
38 Short cuts to AI With billions of images on the web, it s often possible to find a close nearest neighbor. In such cases, we can shortcut hard problems by looking up the answer, stealing the labels from our nearest neighbor.
39 So what is intelligence? Weak AI: The simulation of a mind is a model for the mind. Strong AI: The simulation of a mind is a mind.
40 Chinese Room, John Searle (1980)
41 Chinese Room, John Searle (1980) If a machine can convincingly simulate an intelligent conversation, does it necessarily understand? In the experiment, Searle imagines himself in a room, acting as a computer by manually executing a program that convincingly simulates the behavior of a native Chinese speaker. Most of the discussion consists of attempts to refute it. "The overwhelming majority," notes BBS editor Stevan Harnad, still think that the Chinese Room Argument is dead wrong." The sheer volume of the literature that has grown up around it inspired Pat Hayes to quip that the field of cognitive science ought to be redefined as "the ongoing research program of showing Searle's Chinese Room Argument to be false.
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43 Mechanical Turk von Kempelen, Robotic chess player. Clockwork routines. Magnetic induction (not vision) Toured the world; played Napoleon Bonaparte and Benjamin Franklin.
44 Mechanical Turk It was all a ruse! Ho ho ho.
45 Amazon Mechanical Turk Artificial artificial intelligence. Launched Small tasks, small pay. Used extensively in data collection.
46 Amazon Mechanical Turk Task: Dog? Workers Is this a dog? o Yes o No $0.01 Task Broker Answer: Yes Pay: $0.01
47 Luis von Ahn and Laura Dabbish. Labeling Images with a Computer Game. ACM Conf. on Human Factors in Computing Systems, CHI 2004
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49 Vision (Segmentation): LabelMe Open world database annotated by the community* Notes on Image Annotation, Barriuso and Torralba
50 Utility data annotation via Amazon Mechanical Turk X = $5000 Alexander Sorokin David Forsyth CVPR Workshops 2008 Slides by Alexander Sorokin
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52 Issues Quality? How good is it? How to be sure? Price? Trade off between throughput and cost NOT as much of a trade off with quality Higher pay can actually attract scammers
53 Annotation quality Agree within 5-10 pixels on 500x500 screen There are bad ones. A C E G
54 Ensuring Annotation Quality Consensus / Multiple Annotation / Wisdom of the Crowds Not enough on its own, but widely used Gold Standard / Sentinel Special case: qualification exam Widely used and most important. Find good annotators and keep them honest. Grading Tasks A second tier of workers who grade others Not widely used
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