2019/05/28 by Xavier Ouvrard, Ouvrard, Xavier, Jean‐Marie Le Goff +3
Computer Science · #Advanced Image and Video Retrieval Techniques #Databases (cs.DB) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Image Retrieval and Classification Techniques #Social and Information Networks (cs.SI) #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.1905.11695
openalex publication_date 2019/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need to assess the pertinence of the information retrieved, particularly when s⋅he wants to explore other facets of a multi-facetted information space. For instance, in a multimedia dataset different facets such as keywords, authors, publication category, organisations and figures can be of interest. The facet simultaneous visualisation can help to gain insights on the information retrieved and call for further searches. Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of multisets -- and are in fact linked not only to the publication itself, but to any chosen reference. These references allow to navigate inside the dataset and perform visual queries. We explore here the case of scientific publications based on Arxiv searches.