NTRODUCTION
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), such
as 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 (Makridis and Daras, 2012) such as
(Papaodysseus et al., 2012; Karasik and Smilansky,
2011):
The work by Maiza and Gaildrat (2005) presented
a method based on the profile of the object and
classified it based on a genetic algorithm to evaluate
and determine the optimum position between the
fragment and the tested model. The work by Ying and
Gang (2010) proposed an approach for the
classification of ancient ceramic fragments by relying
on surface texture features that were extracted using a
Gabor wavelet transformation. The classification of
ceramic was performed through applying a nonsupervision kernel- based fuzzy clustering algorithm.
Also, the study of Smith et al. (2010) suggested a
method depending on the color and texture features and
they classified fragments on the basis of the K-Nearest
Neighbor method. The work by Zhou et al. (2011)
involved the classification of ancient porcelain based on
features of color and texture; the fragments were
classified by using the K-Nearest Neighbor method.
Moreover, the work by Makridis and Daras (2012)
focused on the automatic classification of ceramic and
pottery fragments that contain little textual information.
The technique is based on chromaticity and
chrominance (color), the low and medium level
features, as well as the K-Nearest Neighborhood
classifier to classify the fragments.
The main objective of this study is to propose a
novel algorithm for classifying ancient pottery
fragments depending on global feature, specifically the
Res. J. Appl. Sci. Eng. Technol., 14(9): 324-333, 2017
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color feature