Competitive Video Retrieval with vitrivr

Rossetto, Luca; Giangreco, Ivan; Gasser, Ralph; Schuldt, Heiko

doi:10.1007/978-3-319-73600-6_41

Cited by 8 publications

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“…Due to its successful participation to multiple instances of the VBS [18,21], vitrivr is well equipped to handle retrieval challenges in a competitive setting. While the datasets used in the two challenges are quite different [12,23], one of the three task types used in VBS ś the Textual Known-Item Search (KIS) task ś seems to be nearly identical to the setup found during the LSC.…”

Section: Existing Functionality For Lifelog Retrievalmentioning

confidence: 99%

Retrieval of Structured and Unstructured Data with vitrivr

Rossetto

Gasser

Heller

et al. 2019

Proceedings of the ACM Workshop on Lifelog Search Challenge

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With the increase in sensory capability of mobile devices, the data that can be generated and used in a lifelogging context gets increasingly diverse. Such data is special in the context of multimedia, not only because of its close personal relationship with its originator, but also because of its diverse multimodality and its composition from structured, semi-structured, and unstructured data. This diversity poses retrieval challenges that are unique to lifelog data but which also have implications for retrieval activity in other multimedia domains.In this paper, we present the extensions made to the vitrivr opensource multimedia retrieval stack, in order to address some of these unique lifelogging challenges. For the participation to the 2019 Lifelog Search Challenge (LSC), we have extended vitrivr with the capability to process Boolean query expressions alongside contentbased query descriptions in order to leverage the structural diversity inherent to lifelog data.

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