All that video! Analysis Across Time, Place, & Activities
Slide 2 The Problems Too much video Too little time Too few people
Too many hypotheses Hard to search through video Slide 3 Slow
Coding Slide 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 Slide 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 Slide
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? Slide
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? Slide 8 Overlay-viewing Slide 9 Side by Side Slide 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? Slide 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 Slide 12 Scrolling timescales
Slide 13 Place Across Time Microsoft PhotoSynth assembles composite
spaces from large sets of photographs of same or adjacent scenes
from different viewpoints http://photosynth.net/
http://photosynth.net/ 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 Slide 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 Slide 15 Image classification Slide 16 Clustering Faces
Slide 17 Context Browsing Slide 18 Multi-thread Browsing Slide 19
Reductive Comparison Slide 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 Slide 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
