1998
@inproceedings{BCG1998,
vgclass = {refpap},
vgproject = {cbir},
author = {Serge Belongie and Chad Carson and Hayit Greenspan and
Jitendra Malik},
title = {Color- and Texture-based Image Segmentation Using {EM} and
Its Application to Content-Based Image Retrieval},
booktitle = {Proceedings of the International Conference on Computer Vision (ICCV'98)},
address = {Bombay, India},
month = {January},
year = {1998},
url = {http://www.cs.berkeley.edu/\~{}carson/papers/ICCV98.pdf},
url1 = {http://www.cs.berkeley.edu/\~{}carson/papers/ICCV98.ps.gz},
abstract = {Retrieving images from large and varied collections using
image content as a key is a challenging and important problem. In this
paper we present a new image representation which provides a
transformation from the raw pixel data to a small set of image regions
which are coherent in color and texture space. This so-called
``Blobworld'' representation is based on segmentation using the
Expectation-Maximization algorithm on combined color and texture
features. The texture features we use for the segmentation arise from a
new approach to texture description and scale selection.
We describe a system that uses the Blobworld representation to retrieve
images. An important and unique aspect of the system is that, in the
context of similarity-based querying, the user is allowed to view the
internal representation of the submitted image and the query results.
Similar systems do not offer the user this view into the workings of
the system; consequently, the outcome of many queries on these systems
can be quite inexplicable, despite the availability of knobs for
adjusting the similarity metric.},
}