Semantic Image Retrieval via Active Grounding of Visual Situations
IEEE 12TH International Conference On Semantic Computing (ICSC)
We describe a novel architecture for semantic image retrieval-in particular, retrieval of instances of visual situations. Visual situations are concepts such as “a boxing match,” “walking the dog,” “a crowd waiting for a bus,” or “a game of pingpong,” whose instantiations in images are linked more by their common spatial and semantic structure than by low-level visual similarity. Given a query situation description, our architecture-called Situate-learns models capturing the visual features of expected objects as well the expected spatial configuration of relationships among objects. Given a new image, Situate uses these models in an attempt to ground (i.e., to create a bounding box locating) each expected component of the situation in the image via an active search procedure. Situate uses the resulting grounding to compute a score indicating the degree to which the new image is judged to contain an instance of the situation. Such scores can be used to rank images in a collection as part of a retrieval system. In the preliminary study described here, we demonstrate the promise of this system by comparing Situate's performance with that of two baseline methods, as well as with a related semantic image-retrieval system based on “scene graphs.”
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Quinn, M. H., Conser, E., Witte, J. M., & Mitchell, M. (2018, January). Semantic image retrieval via active grounding of visual situations. In 2018 IEEE 12th International Conference on Semantic Computing (ICSC) (pp. 172-179). IEEE.