1.
UPC-UB-STP @ MediaEval 2015 Diversity Task:
Iterative Reranking of Relevant Images
Aniol Lidon, Marc Bolaños, Markus Seidl, Xavier Giró-i-Nieto, Petia Radeva, Matthias Zeppelzauer
St. Pölten University
of Applied Sciences

2.
Ranking by
relevance
Compute
features
Filtering
irrelevant
Diversity
re-ranking
Visual data Textual data
● HybridNet architecture (CNN)
● Trained with ImageNet + Places
● FineTuned by human annotators
(relevant, irrelevant).

3.
Ranking by
relevance
Compute
features
Filtering
irrelevant
Diversity
re-ranking
● Top ranked images are kept.
● 15 ~ 20% best performance.

4.
Ranking by
relevance
Compute
features
Filtering
irrelevant
Diversity
re-ranking
Visual data
Textual data
● CNN fc7 trained on ImageNet
● CNN fc8 trained with Places.
Re-ranking
● Iterative algorithm.
● Selects the most different image
to previously selected ones.
● The most relevant image is the
first element.