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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Query by Semantic Sketch
Organization Unit
  • Luca Rossetto
  • Ralph Gasser
  • Heiko Schuldt
  • English
Institution Cornell University
Series Name
Number :1909.1252
ISSN 2331-8422
Date 2019
Abstract Text Sketch-based query formulation is very common in image and video retrieval as these techniques often complement textual retrieval methods that are based on either manual or machine generated annotations. In this paper, we present a retrieval approach that allows to query visual media collections by sketching concept maps, thereby merging sketch-based retrieval with the search for semantic labels. Users can draw a spatial distribution of different concept labels, such as "sky", "sea" or "person" and then use these sketches to find images or video scenes that exhibit a similar distribution of these concepts. Hence, this approach does not only take the semantic concepts themselves into account, but also their semantic relations as well as their spatial context. The efficient vector representation enables efficient retrieval even in large multimedia collections. We have integrated the semantic sketch query mode into our retrieval engine vitrivr and demonstrated its effectiveness.
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