The objective of this study is to suggest a method for classifying archeological fragments into groups. For
this task, the method suggested begins with conversion of images from their original RGB color to a Hue, Saturation
and Value (HSV) color. From that point forward, a 2D median filtering algorithm is implemented to remove any
resultant noise. Next, each image is separated into six sub-block of equivalent size. In order to extract the feature for
each sub-block, it is represented as a vector intersection of colors between each part of the image and the
corresponding parts of the five remaining images. At this stage, we obtain a vector that consists of the six values for
each image. For the last stage, a Self-Organization Map (SOM) Neural Network classifies the fragments into groups
relying upon their HSV color feature. The algorithm was tested on several images of pottery fragments and the
results achieved demonstrate this approach is promising and is able to cluster fragments into groups with high
precision.
Keywords: Archaeology, fragments, HSV color, SOM, sub-blocks
INTRODUCTION
Technology has effectively contributed to the preservation of cultural wealth through complex automated image processing procedures and several authors have participated in providing many of the approaches for the (semi/automated) reconstruction of unknown broken or torn objects from a large number of irregular fragments (Zhu, 2013; Zhou et al., 2007), suchas archeology, forensics and medical imaging
(Papaodysseus et al., 2012; Youguang et al., 2013). In particular, several researchers are interested in
reassembling archaeological fragments, especially
when exploring archaeological objects that have high
archaeological value for the scholars such as
(Papaodysseus et al., 2012; Leit?o and Stolfi, 2005).
Therefore, it is of great interest that the objects are
reassembled before they are lost or damaged. Artifacts
are often found in archaeological excavation sites and
are randomly mixed with each other. Therefore,
classifying them manually is a difficult and time
consuming task, because they commonly exceed
thousands of fragments. Thus, only a few previous
research works focused on the classification of
fragments