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A Radial Basis Function and Semantic Learning Space Based Composite Learning Approach to Image Retrieval
Konstantin Shkurko, Xiaojun Qi
ICASSP (International Conference on Acoustics, Speech, and Signal Processing), 2007
Abstract: This paper introduces a composite learning approach for
image retrieval with relevance feedback. The proposed
system combines the radial basis function (RBF) based lowlevel
learning and the semantic learning space (SLS) based
high-level learning to retrieve the desired images with fewer
than 3 feedback steps. User's relevance feedback is utilized
for updating both low-level and high-level features of the
query image. Specifically, the RBF-based learning captures
the non-linear relationship between the low-level features
and the semantic meaning of an image. The SLS-based
learning stores semantic features of each database image
using randomly chosen semantic basis images. The
similarity score is computed as the weighted combination of
normalized similarity scores yielded from both RBF and
SLS learning. Extensive experiments evaluate the
performance of the proposed approach and demonstrate our
system achieves higher retrieval accuracy than peer systems.
Files:
paper (pdf, 167 KB)
BibTex (bib, 1.47 KB)
presentation (pdf, 625 KB)
Acknowledgements: This work was supported by the National Science Foundation REU (Research Experience for Undergraduates).
Description: I have participated in the
REU program in Computer Vision and Image Processing at the Department of Computer Science,
Utah State University. I worked on Content-Based Image Retrieval (CBIR) using
relevance feedback and have combined two very different approaches: radial basis
function (RBF) network and semantic space. My mentor, Xiaojun Qi,
and I have proposed a novel way of building the semantic space.
The basic idea was to search through an image database (in our case, 6000
COREL) categorized into different classes, like "Africa," "Elephants,"
"Busses," and return images similar semantically. RBF was used to home in
on relevant images, by taking into account images marked by the user as
relevant and non-relevant. The used features included: HSV color histogram,
first three RGB color moments, and MPEG-7 edge histogram. The Semantic Space
held semantic similarity between images, and was used to update semantic
feature vectors. Combining both of the methods allowed to improve the results
from averaging about 70 - 80% to high 90%, with 30 images returned.