2002
@inproceedings{FaB2002,
vgclass = {refpap},
vgproject = {cbir},
author = {Julien Fauqueur and Nozha Boujemaa},
title = {Image Retrieval by Regions: Coarse Segmentation and Fine
Color Description},
editor = {Shi-Kuo Chang and Zen Chen and Suh-Yin Lee},
booktitle = {Proceedings of the 5th International Conference on Recent
Advances in Visual Information Systems (VISUAL 2002)},
address = {Hsin Chu, Taiwan},
number = {2314},
series = {Lecture Notes in Computer Science},
pages = {24--35},
publisher = {Springer-Verlag},
month = {March~11--13},
year = {2002},
url = {http://www.springerlink.com/link.asp?id=vq2pxdthmd6h0217},
abstract = {In Content-Based Image Retrieval systems, region-based
queries allow more precise search than global ones. The user can
retrieve similar regions of interest regardless their background in
images.
The definition of regions in thousands of generic images is a difficult
key point, since it should not need user interaction for each image,
and nevertheless be as close as possible to regions of interest (to the
user).
In this paper we first propose a new technique of unsupervised coarse
detection of regions which improves their visual specificity. The
Competitive Agglomeration (CA) classification algorithm, which has the
advantage to automatically determine the optimal number of classes, is
used.
The second key point is the region description which must be finer for
regions than for images. We present a novel region descriptor of fine
color variability: the Adaptive Distribution of Color Shades. It is
based on color shades adaptively determined for each region at a high
resolution: 5 million of potential different colors represented against
few hundreds of predefined colors in existing descriptors.
Successful results of segmentation and region queries are presented on
a database of 2500 generic images involving landscapes, people,
objects, architecture, flora\ldots},
}