UPMC at MediaEval 2013: Relevance by Text and Diversity by Visual Clustering
| Autor: | Kuoman Mamani, Christian Antonio, Tollari, Sabrina, Detyniecki, Marcin |
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| Přispěvatelé: | Learning, Fuzzy and Intelligent systems (LFI), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Kuoman Mamani, Christian Antonio |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | MediaEval 2013 Multimedia Benchmark Workshop MediaEval 2013 Multimedia Benchmark Workshop, Oct 2013, Barcelona, Spain |
| Popis: | International audience; In the diversity task, our strategy was to, first, try to improve relevance, and then to cluster similar images to improve diversity. We propose a four step framework, based on AHC clustering and different reranking strategies. A large number of tests on devset showed that most of the best strategies include text based reranking for pertinence, and visual clustering for diversity - even compared to location based descriptors. Results on expert and crowd-sourcing testset grounds truths seem to confirm these observations. |
| Databáze: | OpenAIRE |
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