Archaeological fragments classification based on rgb color and texture features

Nada A. Rasheed, Md. Jan Nordin

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Artifacts are often found in archaeological excavation sites mixed with each other randomly. Therefore, classifying them manually is a difficult task and time consuming because they commonly exceed thousands of fragments. Thus, the aim of this study is to find a solution for classification of ancient pottery into groups by computer assistance. This is a preparatory stage for the next phase, which is the reconstruction of the archaeological fragments with high accuracy. To solve this problem, several steps must be taken, which are image segmentation via a proposed algorithm, and cluster the fragments into groups based on color and texture features. We proposed a novel algorithm that relies on the intersection of the RGB color between the archaeological fragments, and extraction of texture features from fragments based on Gray Level Cooccurrence Matrix (GLCM) that include Energy, Contrast, Correlation and Homogeneity. Finally, by using both proposed algorithm for classifying the color feature, and Euclidean distance for classifying the texture feature, we can classify the fragments with a high accuracy. The algorithm was tested on a pottery database, and it achieved a success rate almost 95%, so we would like to point out that by using the proposed algorithms we achieved promising results.

Original languageEnglish
Pages (from-to)358-365
Number of pages8
JournalJournal of Theoretical and Applied Information Technology
Volume76
Issue number3
Publication statusPublished - 30 Jun 2015

Fingerprint

Archaeology
Texture Feature
Fragment
Textures
Color
High Accuracy
Gray Level Co-occurrence Matrix
Image segmentation
Excavation
Euclidean Distance
Image Segmentation
Homogeneity
Exceed
Intersection
Classify
Energy

Keywords

  • Archaeological objects
  • GLCM
  • Intersection
  • RGB color
  • Texture feature

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Archaeological fragments classification based on rgb color and texture features. / Rasheed, Nada A.; Nordin, Md. Jan.

In: Journal of Theoretical and Applied Information Technology, Vol. 76, No. 3, 30.06.2015, p. 358-365.

Research output: Contribution to journalArticle

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