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A Greek Pottery Shape and School Identification and Classification System

School of Computer Science & Information Systems White Plains, NY. A Greek Pottery Shape and School Identification and Classification System Using Image Retrieval Techniques Gulsebnem (Sheb) Bishop, Sung-Hyuk Cha, Charles Tappert. May 6th, 2005.

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A Greek Pottery Shape and School Identification and Classification System

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  1. School of Computer Science & Information Systems White Plains, NY A Greek Pottery Shape and School Identification and Classification System Using Image Retrieval Techniques Gulsebnem (Sheb) Bishop, Sung-Hyuk Cha, Charles Tappert May 6th, 2005

  2. 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.

  3. Many uses to this system: • The system can serve as an educational tool for novice archaeologists to identify and study artifacts or fragments quickly and easily. • It can serve as a valuable tool in excavations for identification, classification and reconstruction of fragments. • 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. • It can assist in identification and analysis of pottery decorations.

  4. 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: • Shape and color-based image retrieval techniques will be used together for the first time. • Image retrieval from our database is not text based its image based.

  5. DATABASE 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

  6. Alabastron Amphora Group Crater Group

  7. Lekythoi Group Cups Pyxis Hydria-Kalpis Stamnos

  8. Kyathos Kantharos Pelike Oinochoi Skyphos

  9. Schools White Ground 550-330 BC Black Figure 630-530 BC White Ground 460-420 BC Red Figure 530-470 BC

  10. Pottery Identification and Retrieval System – PIRS • We obtain a digital image of our object. • This image goes through a segmentation process. • We then measure the regional properties of this segmented image.

  11. 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

  12. 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.

  13. 5. After the user identifies the matching piece the system outputs information about that piece.

  14. 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.

  15. 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.

  16. 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. • We obtain the image of the fragment. • We segment the image. • A template matching algorithm identifies the top five matches.

  17. Training and Testing Training Set: 200 Images Whole Pottery Testing Set: 400 Images Fragments Testing Set: 400 Images Attention given to 4 issues: • How accurately the system identifies the shapes of the whole vessels? • How accurately the system matches the fragments? • How accurately the system identifies the single fragments? • How accurately the system identifies the color conventions?

  18. System detects the shapes of the selected images with 99% accuracy. Queried Image Top five similar images retrieved Queried Image Top five similar images retrieved

  19. 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 Top five similar images retrieved Queried Image Top five similar images retrieved

  20. 4. The color convention in both, whole and cropped images, was detected with 98% accuracy. Queried Image Top five similar images retrieved

  21. 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.

  22. 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.

  23. 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

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