Intelligent Systems Laboratory
Image Database Retrieval
In this project, we use two feature extraction methods and
two decision methods that allow a user to
input an image or a section of an image and to retrieve all images
from a database having some section in them that is like the user
input image.
The feature extraction methods used are the variances of gray level
spatial dependencies computed from co-occurrence matrices, and the
line-angle-ratio statistics constituted by a 2-D histogram of the
angles between two lines and the ratio of mean gray values inside
and outside the regions spanned by those angles. We also combine these
two sets of features to make use of their different advantages.
A likelihood ratio is defined to measure the relevancy of two images
one being the query image and one being a database image
so that image pairs which had a high likelihood ratio were classified
as relevant and the ones which had a lower likelihood ratio were
classified as irrelevant.
Also k-nearest neighbor rule is used to retrieve k images which have the
closest feature vector to the feature vector of the query image in the
high dimensional feature space.
To evaluate the performance of the algorithms
we use a protocol that translates a frame throughout every image
to automatically construct groundtruth image pairs
for the relevance and irrelevance classes.
As experimental databases we use the
Fort Hood Dataset including 1,000
512x512 gray scale images for the RADIUS project, a remote sensing image
dataset including 90 gray scale LANDSAT and other satellite images with
different sizes, and finally
the COREL
Stock Photo Library 1 including 3,100 images from 31 categories.
Description last modified: May 14, 1998. More recent information
can be found in the papers below.
Related Publications
- Journal Papers:
- Book Chapters:
- Conference Papers:
- Selim Aksoy, Robert M. Haralick,
``
Probabilistic vs. Geometric Similarity Measures for Image Retrieval
,''
in Proceedings of IEEE International Conference on Computer
Vision and Pattern Recognition,
volume 2, pages 357-362, Hilton Head Island, South Carolina,
June 13-15, 2000.
- Selim Aksoy, Robert M. Haralick, Faouzi A. Cheikh, Moncef Gabbouj,
``
A Weighted Distance Approach to Relevance Feedback
,''
in IAPR International Conference on Pattern Recognition,
volume IV, pages 812-815, Barcelona, Spain, September 3-8, 2000.
- Selim Aksoy, Robert M. Haralick,
``
Graph-Theoretic Clustering for Image Grouping and Retrieval
,''
in Proceedings of IEEE International Conference on Computer
Vision and Pattern Recognition,
volume 1, pages 63-68, Fort Collins, Colorado, June 23-25, 1999.
- Selim Aksoy, Robert M. Haralick,
``
Using Texture in Image Similarity and Retrieval
,''
in Proceedings of International Workshop on Texture Analysis
in Machine Vision,
pages 111-117, Oulu, Finland, June 14-15, 1999.
- Selim Aksoy, Robert M. Haralick,
``
A Graph--Theoretic Approach to Image Database Retrieval
,''
in
Lecture Notes in Computer Science, vol. 1614,
as Proceedings of the Third International Conference on
Visual Information Systems,
pages 341-348, Amsterdam, The Netherlands, June 2-4, 1999.
- Selim Aksoy, Robert M. Haralick,
``
Content-Based Image Database Retrieval Using
Variances of Gray Level Spatial Dependencies
,''
in
Lecture Notes in Computer Science, vol. 1464,
as Proceedings of IAPR International Workshop on Multimedia
Information Analysis and Retrieval,
pages 3-19, Hong Kong, August 13-14, 1998.
- Selim Aksoy, Robert M. Haralick,
``
Textural Features for Image Database Retrieval
,''
in Proceedings of IEEE Workshop on Content-Based Access of
Image and Video Libraries, in conjunction with CVPR'98,
pages 45-49, Santa Barbara, CA, June 21, 1998.
- Technical Reports:
- Selim Aksoy, Michael L. Schauf, Robert M. Haralick,
``Content-Based Image Database Retrieval Based on
Line-Angle-Ratio Statistics,''
Tech. Rep., Intelligent Systems Lab., University of Washington,
Seattle, WA, November 1997.
Publications last modified: October 2000.