The Watershed-based Social Events Detection Method with Support from External Data Sources

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The Watershed-based Social Events Detection Method with Support from External Data Sources

  1. 1. Minh S. Dao, Truc-Vien Nguyen, GiuliaBoato, Francesco G.B. De Natale University of Trento, Italy @MediaEval 2012-SED task
  2. 2. Watershed transform Social Event Detection Idea: - People tend to introduce similar annotations for all images associated to the same event -> homogeneous regions - People cannot be involved in more than one event at the same time -> borders among events, stop conditionhttp://cmm.ensmp.fr/~beucher/wtshed.html Target: Target: - Markers and all pixels sharing the - Events and all images associated same characteristic (segmented with these events homogeneous regions) Need: Image that needs to be segmented, Markers, Flood progress (stop-condition)
  3. 3. time (col) ... ...username = «procsilas»dateTaken = «2009-01-17» username = «procsilas» dateTaken = «2009-01-17» ... ... ... ... ... ...username = «sarahamina» username = «sarahamina» username = «sarahamina»dateTaken = «2009-01-13» dateTaken = «2009-01-13» ... dateTaken = «2009-01-12» ... ... ... ... ...username = «Xaf»dateTaken = «2009-01-10» username = «Xaf» dateTaken = «2009-01-10» ... ... ... . . . . . . . . users(row) . . . . ... ... ...username = «sarahamina» username = ... username = ...dateTaken = «2009-01-12» dateTaken = ... dateTaken = ... ... ... ...
  4. 4. photo_url username dateTaken title description tags locationsUT image
  5. 5. Watershed transformHint 1: left- and right- neighbor pixels: http://cmm.ensmp.fr/~beucher/wtshed.htmlcan be flooded from markers Hint 2: one segment of oneWhy? user can be merged to another- People tend to introduce similar segment of another user as annotations for all images associated long as they share the same to the same event. MARKERs- People cannot be involved in more than one event at the same time
  6. 6. www.oxforddicti onaries.comwww.macmillan Tf-idf (keywords dictionary.com + semantic relatedness) Tf-idf (locations + geodistance) - Synonym - Semantic (keywords, relatedness locations) - Fixed terms Geodistance www.froscon.de www.cebit.de -----------------www.allconferences.com Wikiindex.conferencesite.eu – list of cities of a countrywww.tradeshowalert.com - List of public place of a ----------------- city www.ieee.org http://www.infoplease.co www.acm.org m/ipa/A0001769.html (cities and GPS)
  7. 7. Step 1: (time direction) - left- and right- neighbor pixels: be flooded from markers with mergin- condition(time, tags, [locatio n]) - Markers also can be merged if they satisfy merging- condition Step 2: (user direction) - Each chunk of each marker of one user will be merged withUT image other chunks of another users
  8. 8. Discussion- Fixed terms (i.e. technical events that for sure are organized only in Germany, for example CeBIT, FrosCon) can improve Precision- Semantic Relatedness could help to get rid of or decrease influences of «irrelevant» or «low sematic relatedness relevant»- Merging-condition should be improved to increase Recall- Geodistance sphere should be defined for each city/public places- Roles of external data sources are important to prune (keywords, location) markers.- Only English (semantic relatedness, keywords, cities names)
  9. 9. - Visual information- Individual Geodistance sphere- Merging-condition w.r.t features’ influences- Various threshold for tf-idf- More languages (now only English)

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