Examples for query using QBSE and QBVE
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Some examples of Semantic Multinomial
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Quering using single image on Corel50
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| Some examples where QBSE performs better than QBVE. The second row of every query shows the images retrieved by QBSE.
Note, for example, that for the query containing white
smoke and a large area of dark train, QBVE tends to retrieve
images with whitish components, mixed with dark components,
that have little connection to the train theme. |
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Quering using multiple image on Flickr18
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| Examples of multiple-image QBSE
queries. Two queries (for Township and Helicopter) are shown, each combining
two examples. In each case, two top rows presents the single-image QBSE
results, while the third presents the combined query. It illustrates the wide
variability of visual appearance of the images in the Township
class. While single-image queries fail to express the semantic
richness of the class, the combination of the two images allows
the QBSE system to expand indoor market scene and buildings
in open air to an open market street or even a railway
platform. This is revealed, by the SMN of the combined
query, presented below, which is a semantically
richer description of the visual concept Township, containing
concepts (like sky, people, street, skyline) from both
individual query SMNs. |
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Detailed Example showing why QBSE works.
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| Query from class commercial construction with top
QBSE and QBVE matches shown. For QBSE, below each image are also published the
semantic features of highest posterior probability. The semantic features of
largest probability include various words that are clearly related to the
concept of construction. Outside the semantic space, retrieval success is
purely due to the effectiveness of contextual relationships.
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