Query by Semantic Sketch

Abstract

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.

References in corpus (5)

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• YOLOv3: An Incremental Improvement
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
• Towards an All-Purpose Content-Based Multimedia Information Retrieval System