Transcript Slide - Seidenberg School of Computer Science and Information

A Greek Pottery Shape and School
Identification and Classification System
Using Image Retrieval Techniques
Gulsebnem (Sheb) Bishop, Sung-Hyuk Cha, Charles Tappert
School of Computer Science & Information Systems
White Plains, NY
May 6th, 2005
We have successfully developed an
image-based
pottery shape and school
identification system for an
unknown pottery or fragment
to assist archaeologists in
identifying and recording objects
quickly and accurately.
Many uses to this system:
1. The system can serve as an educational tool
for novice archaeologists to identify and study
artifacts or fragments quickly and easily.
2. It can serve as a valuable tool in excavations
for identification, classification and
reconstruction of fragments.
3. There are thousands of pottery fragments
found every year in excavations, and they are
usually discarded without being recorded, yet
alone being classified. This system can provide
a quick, inexpensive and objective way of
documenting and classifying these fragments.
4. It can assist in identification and analysis of
pottery decorations.
Our major task in this study is to identify the
shape and the school of a whole pot or a
fragment at hand, by using shape and
color-based image retrieval techniques.
Our system analyzes and compares extracted
features to determine the top five
matching images and information related
to these images and presents them to the
user for final decision.
What makes this study unique is:
1.
Shape and color-based image retrieval
techniques will be used together for the
first time.
2.
Image retrieval from our database is
not text based its image based.
DATABASE
1.
2.
Two sections:
Images of Pottery with Shape and School
Information
Information about the Extracted Features
Training Database
200 Images
20 Distinct Shapes
4 Color Conventions
Alabastron
Amphora Group
Crater Group
Lekythoi Group
Cups
Pyxis
Hydria-Kalpis
Stamnos
Kyathos
Kantharos
Pelike
Oinochoi
Skyphos
Schools
White Ground
550-330 BC
Black Figure
630-530 BC
Red Figure
530-470 BC
White Ground
460-420 BC
Pottery Identification and
Retrieval System – PIRS
We obtain a digital image of our object.
2. This image goes through a segmentation
process.
3. We then measure the regional properties of this
segmented image.
1.
The regional properties measure object or region
properties in an image and returns them in a
structure array.
8 Regional Measurements
 BoundingBox
 MajorAxisLength
 MinorAxisLength
 EquivDiameter
 Eccentricity
 Orientation
 Solidity
 Extent
3. Once the image is segmented and the features extracted this
information is compared to the information in our database.
4. The aim of the color and shape matching algorithm is to identify the
top five matching pieces.
5. After the user identifies the matching piece the system outputs
information about that piece.
During the excavations archaeologists not only find
whole vases but they also find broken vases
and single fragments. We needed to find a
solution to this problem also.
Fragments belonging to the same pot go through
the same stage.
1. Obtain the image of the fragments.
2. We put the fragments together through
Jigsaw puzzle like algorithms.
2. We segment the image.
3. We extract the features.
4. Compare it to the information that we have in
our database.
5. Identifying the top five matches and present it
to the user.
Jigsaw puzzle problem has been thought of as an
important artificial intelligence search problem.
If one tries to solve the jigsaw puzzle problem
based on shape the solution of the problem
becomes harder. The patterns, colors or
decorations on the fragments help us
tremendously locating the matching pieces. It
reduces the search space by utilizing this
information.
Single Fragment
This last section makes sure that the single
fragments are recorded in the system.
If they have decorations on them or if the profile is
clear they can be matched with similar pieces.
Single fragments go through the same process.
1. We obtain the image of the fragment.
2. We segment the image.
3. A template matching algorithm identifies the
top five matches.
Training and Testing
Training Set: 200 Images
Whole Pottery Testing Set: 400 Images
Fragments Testing Set: 400 Images
Attention given to 4 issues:
1.
How accurately the system
whole vessels?
2.
How accurately the system
3.
How accurately the system
fragments?
4.
How accurately the system
conventions?
identifies the shapes of the
matches the fragments?
identifies the single
identifies the color
1.
System detects the shapes of the selected images with 99%
accuracy.
Queried Image
Queried Image
Top five similar images retrieved
Top five similar images retrieved
2. The system puts together the randomly cropped two dimensional images
with high accuracy and matches it to the corresponding image with 98%
accuracy.
3. When the system was tested with single fragments the accuracy rate
depended on the area that we looked at. If it was an obvious and large enough
area the accuracy rate was 99%. If the area was a less identifiable region the
accuracy rate was 70%.
Queried Image
Queried Image
Top five similar images retrieved
Top five similar images retrieved
4. The color convention in both, whole and
cropped images, was detected with 98%
accuracy.
Queried Image
Top five similar images retrieved
Even though our system yielded good
results there is plenty of future work to be
done:
1. Working with less identifiable parts of the
vases.
2. Working on the speed of the identification
process.
3. Extending the study to subtle shapes.
4. Working with real fragments.
REFERENCES
1. Kampel, M. & Sablatnig, R. Virtual Reconstruction of Broken and Unbroken Pottery. In
Proceedings of the Fourth International Conference on 3-D Digital Imaging and
Modeling, pp. 318-325 (2003).
2. Lengyel, A. Computer Applications in Classical Archaeology. In Proceedings of
Computer Applications in Archaeology. pp. 56-62 (1975).
3. Main, P. The Storage Retrieval and Classification of Artefact Shapes. In Computer
Application in Archaeology. pp. 39-48 (1978).
4. Hall, N. S. and Laflin, S. A Computer Aided Design Technique for Pottery Profiles. In
Computer Applications in Archaeology. pp. 178-188 (1984).
5. Lewis, P. H. and Goodson, K. J. Images, Databases and Edge Detection for
Archaeological Object Drawings. Computer Applications and Quantitative Methods in
Archaeology:149-153 (1990).
6. Durham, P., Lewis, P. H. and Shennan, S. J. Artefact Matching and retrieval Using the
Generalised Hough Transform. In Proceedings of Proceedings of Computer
Applications in Archaeology. pp. 25-30 (1995).
7. Sablatnig R. and Menard C. Computer based Acquisition of Archaeological Finds: The
First Step towards Automatic Classification. In 3rd International Symposium on
Computing and Archaeology. Vol. 1, pp. 429-446 (1996).
8. Kampel, M., Sablatnig, R. and Costa, E. Classification of Archaeological Fragments
using Profile Primitives. In Computer Vision, Computer Graphics and
Photogrammetry - a Common Viewpoint, Proc. of the 25th Workshop of the Austrian
Association for Pattern Recognition (OEAGM). Vol. 147, pp. 151-158, Oldenburg,
Wien, München, 2001.
9. Kampel M. and Sablatnig R. 3D Puzzling of Archeological Fragments. In Proceedings
of the 9th Computer Vision Winter Workshop, pp.31-40 (2004).
10. Leitao, H. C. G. and Stolfi, J. Multiscale Method for Reassembly of Two-Dimensional
Fragmented Objects. In IEEE Trans. On Pattern Analysis and Machine Intelligence, 24
(9), pp.1239-1251 (2002).
11. McBride, J. C. & Kimia, B. B. Archaeological Fragment Reconstruction Using Curve
Matching. In Proceedings of the 2003 Conference on Computer Vision and Pattern
Recognition Workshop, pp. 1-8 (2003).
12. Kong, W. and Kimia, B. B. On Solving 2D & 3D Puzzles Using Curve Matching. In
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, pp. 583-591 (2001).
13. Cha, S-H., Murirathnam, S. Comparing Color Images Using Angular Histogram
Measures. In Proceedings of 5th Joint Conference in Information Sciences, vol. II,
CVPRIP, p.139-142 (2000).
14. H. S. Sawhney, H. S. and Hafner, J. L. Efficient Color Histogram Indexing. In
International Conference on Image Processing, vol. 1, pp. 66-70 (1994).
15. Kampel, M. and Sablatnig, R. Color Classification of Archaeological Fragments. In
International Conference on Pattern Recognition (ICPR'00)-Volume 4, September
(2000) pp. 4771.
16. Hart, E., Cha, S-H. and Tappert, C. Interactive Flag Identification Using Image
Retrieval Techniques. Pace University, SCIC Technical Report, Number 203 (2004).
17. Nagy, G. and Zou, J. Interactive Visual Pattern Recognition. In Proceedings of the
International