A Comparative Study for Known Item Visual Search Using Position Color Feature Signatures

Lokoč, Jakub; Kuboň, David; Blažek, Adam

doi:10.1007/978-3-319-51814-5_1

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…The approach of sketch-based retrieval of visual content such as images or videos has been in use for many years as a way to reduce the media discontinuity that occurs when text (or another non-visual modality) is used for query expression. Earlier approaches primarily rely purely on low-level features, such as properties extracted from edge [12,28] or color [3,17,23] information. Either independently or in conjunction with these sketches, semantic information can be expressed by keywords or descriptions that are derived from manual or machine generated annotations [14,31].…”

Section: Related Workmentioning

confidence: 99%

Query by Semantic Sketch

Rossetto¹,

Gasser²,

Schuldt³

2019

Preprint

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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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