Joint work with Daniel Manchon-Vizuete (Pixable) More details: https://imatge.upc.edu/web/publications/upc-mediaeval-2013-social-event-detection-task

1. UPC @ MediaEval 2013
Social Event Detection (Task 1)
Daniel Manchón-Vizuete
Xavier Giró-i-Nieto
Barcelona, Catalonia
19th October 2013

2. Motivation

3. Challenge

4. Challenge

5. Related work
PhotoTOC
[Platt et al, PACRIM 2003]

6. Approach
(a) Temporal sorting by each user independently
Hi, I’m John.
Hi, I’m Emily.

7. Approach
(b) Temporal-based oversegmentation in mini-clusters
PhotoTOC
[Platt et al, PacRim 2003]

8. Approach
(b) Temporal-based oversegmentation in mini-clusters

9. Approach
(c) Sequential merging of mini-clusters
avg(·)
avg(·)
?
avg(·)
avg(·)
t

10. Approach
(c) Sequential merging of mini-clusters
Weighted
modalities
●
●
●
●
creation (or upload) time
geolocation
textual labels
same user

11. Approach
(c) Sequential merging of mini-clusters
Time stamp (d=L1)
Text labels (d=Jaccard)
Geolocation (d=haversine)
Same user (d=boolean)

12. Approach
(c) Sequential merging of mini-clusters
Weighting factors (wi)
Time
Learned
weights
GPS
Labels
User
0.06
0.28
0.22
0.44
0.08
-
0.30
0.60

13. Approach
(c) Sequential merging of mini-clusters
Average and
deviation learned
on pairs of photos
within the same
training event.
z-score

14. Approach
(c) Sequential merging of mini-clusters
phi
function

15. Approach
(c) Sequential merging of mini-clusters
decision
threhold

16. Approach
(c) Sequential merging of mini-clusters

17. Results (required only)
F1
NMI
Divergence F1
Heuristic
weights (*)
0.8798
0.9720
0.8268
Learned
weights
0.8833
0.9731
0.8316
(*) wtime=0.2, wgeo=0.2, wtext=0.2, wuser=0.4,

18. Conclusions
●
Fast solution due to time-sequential nature.
●
Divide and conquer.
●
Little gain with this optimisation approach.
●
Intuition: Thresholds should be event-dependent.
@DocXavi
#mediaeval13
Thank you
MediaEval
SED !