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Summary: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 7, JULY 2009 1645
An Adaptable Image Retrieval System
With Relevance Feedback Using Kernel
Machines and Selective Sampling
Mahmood R. Azimi-Sadjadi, Senior Member, IEEE, Jaime Salazar, and Saravanakumar Srinivasan
Abstract--This paper presents an adaptable content-based
image retrieval (CBIR) system developed using regularization
theory, kernel-based machines, and Fisher information measure.
The system consists of a retrieval subsystem that carries out
similarity matching using image-dependant information, multiple
mapping subsystems that adaptively modify the similarity mea-
sures, and a relevance feedback mechanism that incorporates user
information. The adaptation process drives the retrieval error to
zero in order to exactly meet either an existing multiclass classifi-
cation model or the user high-level concepts using reference-model
or relevance feedback learning, respectively. To facilitate the
selection of the most informative query images during relevance
feedback learning a new method based upon the Fisher infor-
mation is introduced. Model-reference and relevance feedback
learning mechanisms are thoroughly tested on a domain-specific
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