2011
@inproceedings{MoS2011,
vgclass = {refpap},
author = {Nabeel Mohammed and Squire, David McG.},
title = {Effectiveness of {ICF} features for collection-specific
{CBIR}},
booktitle = {Proceedings of the 9th International Workshop on Adaptive
Multimedia Retrieval, co-located with the 22nd International Joint
Conference on Artificial Intelligence (IJCAI 2011)},
address = {Barcelona, Spain},
number = {7836},
series = {Lecture Notes in Computer Science},
pages = {83--95},
publisher = {Springer-Verlag},
month = {July~18--19},
year = {2011},
doi = {http://dx.doi.org/10.1007/978-3-642-37425-8_7},
url = {/publications/postscript/2011/AMR2011.pdf},
abstract = {This study aims to find more effective methods for
collection-specific CBIR. A lot of work has been done in trying to
adapt a system by user feedback, in this study we aim to adapt CBIR
systems for specific image collections in an automated manner.
Independent Component Analysis (ICA), a high order statistical
technique, is used to extract Independent Component Filters (ICF) from
image sets. As these filters are adapted to the data, the hypothesis is
that they may provide features which are more effective for
collection-specific CBIR. To test this question, this study develops a
methodology to extract ICF from image sets and use them to extract
filter responses. In developing this method, the study uses image
cross-correlation and clustering to solve issues to do with
shifted/duplicate filters and selecting a smaller set of filters to
make CBIR practical. The method is used to generate filter responses
for the VisTex database . The filter response energies are used as
features in the GNU Image Finding Tool (GIFT). The experiments show
that features extracted using ICF have the potential to improve the
effectiveness of collection-specific CBIR, although some more work in
this area is required.},
}