Unsupervised Rank Fusion for Diverse Image Metasearch

  • José Solenir L. Figuerêdo UEFS
  • Rodrigo Tripodi Calumby UEFS


For a given query and a set of images ranked lists retrieved from multiple search engines, the metasearch technique aims at combining these lists to build an unified ranking with improved relevance. Rank aggregation is an approach that has been widely used to support this task. This paper investigates the use of rank aggregation methods in the metasearch scenario for diverse image retrieval. Although metasearch systems are usually driven by the relevance of the final result, the impact on diversification has also been analyzed. The experimental findings suggest metasearch based on rank aggregation allows significant improvements, both in terms of relevance and diversity.

Palavras-chave: Rank Aggregation, Diversity, Image Retrieval, Metasearch


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FIGUERÊDO, José Solenir L.; CALUMBY, Rodrigo Tripodi. Unsupervised Rank Fusion for Diverse Image Metasearch. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 265-270. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2019.8834.