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All that video! Analysis Across Time, Place, & Activities.

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Presentation on theme: "All that video! Analysis Across Time, Place, & Activities."— Presentation transcript:

1 All that video! Analysis Across Time, Place, & Activities

2 The Problems  Too much video  Too little time  Too few people  Too many hypotheses  Hard to search through video

3 Slow Coding

4 Why so Much?  Timescales of phenomena of interest  Weeks, months, years of video  Following people across sites & activities  Comparative cases multiply video footage  Video is becoming cheap and easy  Storage and processing is feasible

5 New Hope for the Dread  Winnow footage to identify useful segments  Speedview through footage  Simultaneous multiple video displays  Time sampling  Multiple timescale viewing (temporal zoom)  Place synthesis (multiple viewpoints)  Computational search and classification

6 Time Sampling  Randomly sample with a fixed-length time window  Sample every n seconds for fixed time  Catenate the samples and run as a meta-video  Cf. time-lapse and stroboscopic photography  What makes a sample “representative”?  Criteria for helpful sampling?

7 SpeedViewing  What do we see if we watch a lot of video at accelerated speed?  With or without time-sampling  Manovich group NBC News meta-video: time- sampled to just opening scenes, collected for 20 years, run as a single video  Pattern recognition?  Aided by comparison?

8 Overlay-viewing

9 Side by Side

10 Multiple Comparisons  We are a bit used to watching two related videos side by side  What if there were a display matrix of 4?  Or 9, in a 3-by-3 array?  All running simultaneously, in synch  What would we notice?  How would we learn to watch such displays?

11 Time Zoom  Not side-by-side in space, but nested in time  Along a timeline of the long scale of a video or several chained together,  Expand an inner timeline of an embedded episode,  And within that one, another  Perhaps down to individual frames

12 Scrolling timescales

13 Place Across Time  Microsoft PhotoSynth assembles composite spaces from large sets of photographs of same or adjacent scenes from different viewpoints   If the images were video stills from one or more traversals through a neighborhood, a composite image could index the videos spatially  And allow us to search a video corpus spatially,  With or without GPS markup

14 Computational Screening  Recent advances in computer science support scene recognition in video (TRECVID)  Image and video classification  Find more like these  Identify similarity/difference clusters  Even with many false positives, aids manual segment selection for further analysis

15 Image classification

16 Clustering Faces

17 Context Browsing

18 Multi-thread Browsing

19 Reductive Comparison

20 The Meaning Problem  Maintaining a focus on meaning makes the big picture hard to see and the analysis of large databases of rich media intractable  Postponing a focus on meaning allows us to benefit from the rich redundancy of media features to let computation aid selection  Meaning enters when we select criterial features of interest to guide computation and when we interpret its results afterwards

21 A New Paradigm?  Mixed and Complementary Methods to combine Qualitative & Quantitative paradigms  Abandon the logic of Experimental research on complex socio-natural systems (neither control nor generalizability is achievable)  Keep quantitative methods for data mining qualitatively rich media databases  Keep and extend Qualitative paradigms to ground the logic of research


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