Department of Computer Science and
University of Washington
Multimedia Information Retrieval
This research is supported by the National Science Foundation under
grant number DBI-0543631. Any opinions, findings and conclusions or
recommendations expressed in this material are those of the authors and
do not necessarly reflect the views of the National Science Foundation.
Scientific research in the biological domain generates massive amounts
of data of many different kinds. With a hypothesis to investigate,
researchers run large numbers of experiments that use data
from human and animal subjects and produce multiple outputs of
different modalities ranging from simple textual data, to signal,
image, and 3D volumes such as CT and MRI scans.In spite of the massive
scale and complexity, many researchers at the forefront of biological
sciences are using antiquated methods for storing their multimedia
data. Data are often kept in multiple locations including computers,
notebooks, and file drawers.
The goal of this research is to develop a unified methodology for
organization and retrieval of biological data from scientific
experiments. Our work builds on existing work in experiment
management, approximate queries, and content-based image retrieval. We
are developing a query framework for multimedia data that provides
users with a unified way to access multiple types of
data. Queries will be able to handle both single data types and
multiple related data types, such as registered CT and MRI scans or
neuronal firing patterns and related fMRI data. The data will
be organized in a way that is both easy for users to understand and
efficient query access. A prototype system will be built and evaluated
on thee different applications: a study of language sites in the human
brain, an analysis of the relationship of cataract formation to genetic
factors, and a study of craniofacial disorders in children.
| Linda Shapiro, PI
| James Brinkley, co-PI
| Dan Suciu, co-PI
|Sara Rolfe, RA
| Indriyati Atmosukarto, RA
|Jia Wu, RA
|Lynn Yang, RA
|Kasia Wilamowska, RA
| Ravensara Travillian
M. Gubanov, A. Pyayt, L. G. Shapiro,
"ReadFast: Browsing large documents through Unified Famous Objects,"
Proceedings of the 12th
IEEE International Conference on Information Reuse and Integration
(IRI), Las Vegas, Nevada, 2011.
M. Gubanov, L. G. Shapiro, A. Pyayt,
"Learning Unified Famous Objects (UFO) to Bootstrap Information
of the 12th IEEE International Conference on Information Reuse and
Integration (IRI), Las Vegas, Nevada, 2011.
I. Atmosukarto, L. G. Shapiro, C. Heike, "Use of Genetic Programming
for Learning 3D Craniofacial Shape
Quantification", ICPR 2010.
I. Atmosukarto and L. G. Shapiro,
"3D Object Retrieval Using Salient Views", ACM Multimedia Information
Atmosukarto, L. G. Shapiro, J. R. Starr, C. L. Heike, B. Collett, M. L.
M. L. Speltz, "3D Head Shape Quantification for
Infants with and without Deformational Plagiocephaly", The Cleft-Palate
Craniofacial Journal, 2009.
Atmosukarto, K. Wilamowska, C. Heike, L. G. Shapiro. "3D Object
Classification using Salient
Patterns With Application to Craniofacial Research, Pattern
Recognition, Vol. 43, No. 4, 2010, pp. 1502-1517.
R. F. Tungaraza, J. Guan, L. G.
Shapiro, J. F. Brinkley, J. Ojemann, and J. D. Franklin, "A Similarity
Retrieval Tool for Functional Magnetic Resonance Imaging (fMRI)
Statistical Maps," Artificial Intelligence in Medicine, to appear 2009.
R. F. Tungaraza, J. Guan, S. Rolfe, I.
Atmosukarto, A. Poliakov, N. M. Kleinhans, E. Aylward, J. Ojemann, J.
F. Brinkley, L. G. Shapiro, "A Similarity Retrieval Method for
Functional Magnetic Resonance Imaging (fMRI) Statistical Maps," SPIE
Medical Imaging: Image Processing, 2009.
L Shapiro, K Wilamowska, I Atmosukarto, J Wu,
CL Heike, M Spelz, and M Cunningham. "Shape-Based Classification of 3D
Head Data." International Conference on Image Analysis and Processing,
J. Wu, K. Wilamowska, L. Shapiro, C. Heike, "Automatic Analysis of Local Nasal Features in
2q11.2DS Affected Individuals," IEEE EMBS, 2009.
K Wilamowska, L Shapiro, and CL Heike.
"Quantification of 3D face shape in 22q11.2 deletion syndrome". IEEE
International Symposium on Biomedical Imaging, 2009.
S. M. Rolfe, L. Finney, R. F. Tungaraza,
J. Guan, L.G. Shapiro, J.F. Brinkely, A. Poliakov, N. Kleinhans, E.
Alyward, "An independent component analysis based tool for exploring
functional connections in the brain," SPIE Medical Imaging: Image
I. Atmosukarto, L. Shapiro, M.
and M. Speltz. "Automatic 3D Shape Severity Quantification and
Localization for Deformational Plagiocephaly". In Proc. SPIE
Medical Imaging: Image Processing, 2009.
S.Yang, I. Atmosukarto, J. Franklin, J.
F. Brinkley, D. Suciu, and L. G. Shapiro,"A Model of Multimodal Fusion
for Medical Applications," SPIE Multimedia Content Access: Algorithms
and Systems III, 2009.
J-H. Chen and L. G. Shapiro, "Medical
image segmentation via min s-t cuts with sides constraints", ICPR, 2008.
I. Atmosukarto and L. G.
Shapiro, " A
Learning Approach to 3D Object Classification", S+SSPR, 2008.
I. Atmosukarto and L. G.Shapiro, "A
Salient-Point Signature for 3D
Object Retrieval", ACM Multimedia Information Retrieval, 2008.
I. Atmosukarto, R. Travillian,
J. Franklin, L. Shapiro, J. Brinkley, D. Suciu, J. Clark, M.
Cunningham, "A Unifying
Combining Content-Based Image Retrieval with Relational Database
Biomedical Applications", Annual
Meeting of the Society for Imaging Informatics in Medicine, 2008.
L. G. Shapiro, I. Atmosukarto, H. Cho,
H. J. Lin, S. Ruiz-Correa, and J. Yuen, "Similarity-Based Retrieval for
Biomedical Applications", Case-Based
Reasoning on Signals and Images. P. Perner (Ed.), Springer, 2007.
S. Rolfe, An Independent
Analysis Tool for Exploring Functional Connections in the Brain,
MS Thesis, Electrical Engineering Department, University of Washington,