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 !