Visual instance search from one example
10 January 2017
Number of pages
Faculty of Science (FNWI)
Informatics Institute (IVI)
A visual instance is a visually unique entity (e.g., Brooklyn bridge), or a set of entities with identical visual appearance
and hence not visually distinguishable (e.g., a Miffy doll with many identical copies). The same instance may vary tremendously
in appearance in different images due to self-deformation, occlusion, illumination variations, viewpoint change and other
recording factors. This thesis is dedicated to visual instance search from one example. Given one image of an instance, the
goal is to retrieve all the images of the same instance, regardless of the appearance variations in different recordings.
The main findings are as follows. 1) Localized search by evaluating multiple local boxes in the image is advantageous, as
the spatial extent of an instance is often limited to a portion of an image and the signal-to-noise ratio within the box is
higher than in the entire image. 2) A strict matching on local details is helpful for one-sided instances with limited viewpoint
variations, e.g., logos, but not suited for multi-sided instances like shoes, when photographed from every possible viewing
angle. 3) The attribute-based method, which learns a set of visual aspects, is generically applicable to both one-sided and
multi-sided instances. The learned aspects are invariant to recording factors and discriminative among different instances.
These aspects are generalizable to previously unseen instances. 4) Tracking, when provided with a powerful similarity metric,
can be addressed as an instance search problem where the initial target in the starting frame is considered as the query.
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