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UPC @ MediaEval 2014Social Event Detection (Task 1)
Daniel Manchón-VizueteIrene Gris-SarabiaXavier Giró-i-Nieto
Barcelona, Catalonia16th October 2014
Related work
PhotoTOC[Platt et al, PACRIM 2003]
Approach: (a) Temporal sorting by each user independently
Hi, I’m John. Hi, I’m Emily.
PhotoTOC[Platt et al, PacRim 2003]
(b) Temporal-based oversegmentation in mini-clustersApproach:
NEW!!(c) Mini-cluster representation (only text)Approach:
t
title: Darklord Dubload Project tags:[ eras, lessnesses, los angeles, pehrspace, fantastic]
title: Lessnesses!!tags:[ eras, lessnesses, los angeles, pehrspace, snorlax, marvellous ]
title: Snorlaxtags: [ lessnesses, los angeleeees, pehrspace]
NEW!!(c) GPS reverse geocodingApproach:
t
gps: 34.0663, -118.26 gps = "" gps: 34.0, -118.0
Reverse geocoding
gps=[333, Laveta, Terrace, Los, Angeles, CA, 90026, EE. UU.]
gps=[] gps=[ Los, Angeles, CA, 90026, EE. UU.]
NEW!!(c) Hypernyms enrichmentApproach:
t
tags:[ eras, lessnesses, los angeles, pehrspace, fantastic]
tags:[ eras, lessnesses, los angeles, pehrspace, snorlax, marvellous ]
tags: [ lessnesses, los angeles, pehrspace, rock]
Hypernyms
tags:[ eras, lessnesses, los angeles, pehrspace, fantastic,great,marvellous]
tags:[ eras, lessnesses, los angeles, pehrspace, snorlax, marvellous, great,fantastic]
tags: [ lessnesses, los angeles, pehrspace, rock, music, band]
Approach (c) Mini-cluster representation
tags:[ eras, lessnesses, los angeles, pehrspace, fantastic,Darklord, Dubload Project ,333, Laveta, Terrace, Los, Angeles, CA, 90026, EE. UU.,great,marvellous]
tags:[ eras, lessnesses, los angeles, pehrspace, snorlax, marvellous,Lessnesses!!,Snorlax,essnesses, los angeles, pehrspace,great,fantastic,music, band,Los, Angeles, CA, 90026, EE. UU.]
Hypernyms
Titles
GPS reverse geocoding
Approach
t
(d) TF-IDF and cosine distance
d>γ1
d>γ2
N1
N2N1
... ...
NEW!!
Near neighbours (N1)
Distant neighbours (N2)
Resulting clusters
Results
Reverse Geocoding
Hypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
Reverse Geocoding
Hypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
ResultsReverse
GeocodingHypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
º
Sponsored by:Reverse Geocoding
Hypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
º
Reverse Geocoding
Hypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
º
F1=0.883 F1=0.9242013 2014
Reverse Geocoding Hypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
Reverse Geocoding Hypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
Reverse Geocoding Hypernym F1 NMI Div. F1
0.9240 0.9820 0.9231
0.9165 0.9793 0.9155
0.8141 0.9432 0.8127
0.8112 0.9393 0.8097
Future work
● Enrichment with visual features● Image to text with deep learning tecniques● Caffe library pre-trained for Imagenet
Conclusions● Fast solution due to time-sequential nature.
● Geolocation and hypermyns doesnt improve result
● Divide and conquer.
Thank you MediaEval
SED !