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CERTH @ MediaEval 2012 SocialEvent Detection TaskManos Schinas, Georgios Petkos, Symeon Papadopoulos,Yiannis KompatsiarisP...
The problem•   Identify social events in tagged photos collections:    –   Challenge 1: Technical Events @ Germany    –   ...
Approach Step 1 Step 2 Step 3           3
Graph Creation (1)• Graph creation is based on the use of “Same  Class” model  – A classifier which predicts whether two i...
Graph Creation (2)• Use the same class model to connect the items  of the collection that belong to the same event• Retrie...
Event Partitioning and Expansion (1)• Event partitioning  – The nodes of the graph are clustered into    candidate events ...
Event Partitioning and Expansion (2)• Expansion of the candidate events set  – Each image that does not belong to any even...
Event Filtering (1)• Filter in two ways:  – By using geo-location (if exists)  – By using tag-based models• Geo-location F...
Event Filtering (2)• Tag-based filtering  – Build term models by finding the 500 dominant    terms for the specific locati...
Event Filtering (3)• Tag-based filtering  – Probability of appearance  – We compute the ratio of the probability of    app...
EvaluationNotationRun 1: Same class model trained with 10000 pairs of images.Run 2: Same class model trained with 30000 pa...
Discussion (1)• Moving from a smaller (run 1) to a larger (run  2) training dataset does not seem to improve  most of the ...
Discussion (2)• Future actions:  – train the same class model with a richer set of    data  – explore different graph cons...
Questions            14
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CERTH @ MediaEval 2012 Social Event Detection Task

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CERTH @ MediaEval 2012 Social Event Detection Task

  1. 1. CERTH @ MediaEval 2012 SocialEvent Detection TaskManos Schinas, Georgios Petkos, Symeon Papadopoulos,Yiannis KompatsiarisPisa, 4-5 October 2012
  2. 2. The problem• Identify social events in tagged photos collections: – Challenge 1: Technical Events @ Germany – Challenge 2: Soccer matches @ Madrid, Hamburg – Challenge3: Indignados protest @ Madrid• Alternative formulation: – Represent a collection of photos as a graph, where items with high probability to belong to the same event are connected. – Each event forms a dense sub-graph in it. – Points to community detection as method to address the problem. 2
  3. 3. Approach Step 1 Step 2 Step 3 3
  4. 4. Graph Creation (1)• Graph creation is based on the use of “Same Class” model – A classifier which predicts whether two images belong to the same event or not – Support Vector Machine classifier trained with the data of the 2011 challenge – Input features: dissimilarities across user, title, tags, description, time taken, GIST, SURF/VLAD 4
  5. 5. Graph Creation (2)• Use the same class model to connect the items of the collection that belong to the same event• Retrieve candidate neighbours (~350) to reduce computational cost – 50 with respect to textual features – 150 with respect to time – 50 with respect to location (when it exists) – 100 with respect to visual features 5
  6. 6. Event Partitioning and Expansion (1)• Event partitioning – The nodes of the graph are clustered into candidate events by using the Structural Clustering Algorithm for Networks (SCAN). – The items clustered together by SCAN are used to obtain an aggregate representation of each candidate social event. – Split the candidate events that exceed a predefined time range into shorter events. 6
  7. 7. Event Partitioning and Expansion (2)• Expansion of the candidate events set – Each image that does not belong to any event forms a single-item event. – Merge these single-item events into larger clusters by checking location and time. – Add the new events in the set of the candidate events 7
  8. 8. Event Filtering (1)• Filter in two ways: – By using geo-location (if exists) – By using tag-based models• Geo-location Filtering – Discard events that don’t contained into the bounding box of the specific challenge – 30% of candidate events are discarded 8
  9. 9. Event Filtering (2)• Tag-based filtering – Build term models by finding the 500 dominant terms for the specific locations and event types. – we collect images from Flickr that are relevant to the location or the type of event of interest. – Images for Madrid, Hamburg and Germany – Images for indignados, soccer and technical events 9
  10. 10. Event Filtering (3)• Tag-based filtering – Probability of appearance – We compute the ratio of the probability of appearance in the focus set over the probability of appearance in the reference set. – Keep the 500 terms with the highest ratio – Jaccard similarity between a tag model and events terms 10
  11. 11. EvaluationNotationRun 1: Same class model trained with 10000 pairs of images.Run 2: Same class model trained with 30000 pairs of images.Run 3: Same class model of run 1 with post processing step 11
  12. 12. Discussion (1)• Moving from a smaller (run 1) to a larger (run 2) training dataset does not seem to improve most of the performance  over fitting• Method fails in challenge 1 because these events are different from these of the training dataset• A good tag model has to be used for classification in post-filtering step 12
  13. 13. Discussion (2)• Future actions: – train the same class model with a richer set of data – explore different graph construction strategies and community detection algorithms.• Ways to improve: – better topic classification methods – more sophisticated methods for location estimation 13
  14. 14. Questions 14

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