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UPC-UB-STP @ MediaEval 2015 diversity task iterative reranking of relevant images

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https://imatge.upc.edu/web/publications/upc-ub-stp-mediaeval-2015-diversity-task-iterative-reranking-relevant-images

This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task.The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.

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UPC-UB-STP @ MediaEval 2015 diversity task iterative reranking of relevant images

  1. 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. 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. 3. Ranking by relevance Compute features Filtering irrelevant Diversity re-ranking ● Top ranked images are kept. ● 15 ~ 20% best performance.
  4. 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.

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