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Project – Probabilistic Relevance Feedback for Image Retrieval
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Motivation and Goal |
The goal is to
retrieve images in database that are similar to the query the user has in
mind. Relevance feedback is a technique
to let the user interact with the system by giving examples so that the system
has more information of what the user needs.
The key is how to make the best out of the feedback information.
More formally, we
define our problem as follows: Given the database images that the user
considers similar to a query, rank all database images from most to least
similar to the query. It is worth
mentioning that the query does not have to physically exist, but can be a
concept the user has in mind. All the
system needs are the images that the user identified as similar or relevant to
the query.
All the logo images
in the database have been labeled with design search
paths. Design search paths were assigned
by trademark and patent experts. Based
on design search paths, we build feature vectors, which we call high-level
semantic feature vectors. They are
binary vectors, each dimension being a feature representing the presence
(feature value equal to 1) or absence (equal to 0) of a specific object,
concept, or shape.
We present a
procedure to retrieve objects based on the high-level semantic feature
vectors. The assumption is, if two high-level semantic feature vectors are relevant,
then they share at least one non-absent feature.
- D. Liu and T. Chen, “Probabilistic relevance feedback for image retrieval with binary feature vectors,” IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2005.
Any suggestions or comments are welcome. Please send them to David Liu.