VIR (Visual Information Retrieval) is a library for content based image retrieval and classification based on global and local features. The library allows comparing images considering their global and/or local features. It includes local features matching, RANSAC, MPEG-7 global features comparisons, kNN classification, Bag-of-Words (or Bag-of-Features) approach. It is an ongoing project.
The Story Map Building and Visualization Tool (SMBVT) is a semi-automatic tool to construct and visualise narratives, intended as semantic networks of events related to each other through semantic relations. This tool obeys an ontology for narratives we developed. SMBVT uses Wikidata as a reference knowledge base for importing entities, and Wikimedia Commons as an external source of images. The user of SMBVT can build each event of the narrative and attach to them the participating entities from Wikidata, or they can define new ones not present in the knowledge base. SMBVT assigns IRIs (Internationalised Resource Identifiers) to the selected instances and facilitates the construction of events based on these instances, and their linking to form narratives. The knowledge collected by the tool is exported as Linked Data, and the knowledge is automatically saved, behind the scenes, as an OWL (Web Ontology Language) graph in a Fuseki triple store. Based on this representation, the tool allows the user to visualise the knowledge in the graph in simple formats like story maps, timelines and network graphs, using SPARQL queries. The tool is open source at: https://github.com/EmanueleLenzi92/SMBVT