A Greek Pottery Shape and School Identification and Classification System

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• 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
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• 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.

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• 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.

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• 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.

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• 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

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Alabastron
Amphora Group
Crater Group
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Lekythoi Group
Cups
Pyxis
Hydria-Kalpis
Stamnos
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Kyathos
Kantharos
Pelike
Oinochoi
Skyphos
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Schools

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

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• 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

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

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• 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.

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• 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.

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• 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?

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1. 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
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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
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• 4. The color convention in both, whole and
  cropped images, was detected with 98 accuracy.

Queried Image Top five similar images
retrieved
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• 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.

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