Department of Computer Science and Engineering
University of Washington
Content-Based Image Retrieval
With large image databases becoming a reality both in
scientific and medical domains and in the vast advertising/marketing
domain, methods for organizing a database of images and for
efficient retrieval have become important. We have worked on three different
aspects of this problem.
1. Image Indexing
Andy Berman's 1999 Ph.D. thesis on Efficient Content-Based Image Retrieval
was a seminal work that developed new indexing techniques for image databases
using images as the indices. In this work, the triangle inequality for metrics
was used to compute lower bounds for both simple and compound distance measures.
These bounds allowed the retrieval system to rule out large portions of the database
and to order the remaining images approximately according to their similarity to
the query.
Publications:
A. Berman and L. G. Shapiro, "A Flexible Image Database System
for Content-Based Retrieval," Computer Vision
and Image Understanding, Vol. 75, Nos. 1-2, 1999, pp. 175-195.
Demos:
FIDS Demo
2. Object Recognition for Image Retrieval
Yi Li's dissertation in 2005 developed two new learning paradigms for
object recognition in the context of content-based image retrieval.
Both paradigms use the concept of an abstract regions as the
basis for recognition. Abstract regions are image regions that can be
obtained from the image by any computational process, such as color
segmentation, texture segmentation, or interest operators. The first
learning algorithm was a generative approach that developed an EM Classifier
that learned Gaussian models for different classes of objects. The second
learning algorithm was a more powerful Generative/Discriminative Approach
that began with EM clustering and used the clusters (in each feature space)
to construct fixed-length feature vectors that described each image in
terms of its response to each of the components. The feature vectors were then
used to train a classifier to recognize the object class or concept being
learned.
Publications:
-
Y. Li, L. G. Shapiro and J. A. Bilmes, "A Generative/Discriminative
Learning Algorithm for Image Classification", International Conference on Computer
Vision, October 2005, pp. 1605-1612.
-
Y. Li, J. Bilmes, and L. G. Shapiro, "Object Class Recognition using
Images of Abstract Regions," Proceedings of the International
Conference on Pattern Recognition, Vol. 1, 2004, pp. 40-43.
-
"Object Recognition for Content-Based Retrieval,"
Invited talk at Dagstuhl Seminar on Content-Based
Image and Video Retrieval, January, 2002.
-
Li, Y. and L. G. Shapiro,
"Consistent Line Clusters for Building Recognition in CBIR",
Proceedings of the International Conference on Pattern
Recognition, 2002, pp. 952-6.
Demos:
Object Recognition Demos
Software:
Object Recognition
Software
Ground Truth Database:
Annotated Image Database
