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Semantic Image Retrieval via Active Grounding of Visual Situations
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 ping-pong," 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.
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
The knowledge representation community has built general-purpose ontologies
which contain large amounts of commonsense knowledge over relevant aspects of
the world, including useful visual information, e.g.: "a ball is used by a
football player", "a tennis player is located at a tennis court". Current
state-of-the-art approaches for visual recognition do not exploit these
rule-based knowledge sources. Instead, they learn recognition models directly
from training examples. In this paper, we study how general-purpose
ontologies---specifically, MIT's ConceptNet ontology---can improve the
performance of state-of-the-art vision systems. As a testbed, we tackle the
problem of sentence-based image retrieval. Our retrieval approach incorporates
knowledge from ConceptNet on top of a large pool of object detectors derived
from a deep learning technique. In our experiments, we show that ConceptNet can
improve performance on a common benchmark dataset. Key to our performance is
the use of the ESPGAME dataset to select visually relevant relations from
ConceptNet. Consequently, a main conclusion of this work is that
general-purpose commonsense ontologies improve performance on visual reasoning
tasks when properly filtered to select meaningful visual relations.Comment: Accepted in IJCAI-1
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