Abstract

Normally, the artifacts are found in a fractured state and mixed randomly and the process of manual classification may requires a great deal of time and tedious work. Therefore, classifying these fragments is a challenging task, especially if the archaeological object consists of thousands of fragments. Hence, it is important to come up with a solution for the classification of the archaeological fragments accurately into groups and reassembling each group to original form by using computer techniques. In this study we interested to find the solve to this problem depending on color and texture features, to accomplish that the algorithm begins by partition the image into six sub-blocks. Furthermore, extract HSV color space feature from each block, then this feature represent into a cumulative histogram, as a result we obtain six vectors for each image. Regard to extract the texture feature for each sub-block it will be used the Gray Level Co-occurrence Matrix (GLCM) that include Energy, Contrast, Correlation and Homogeneity. At the final stage, based on k-Nearest Neighbors algorithm (KNN) classifies the color and texture features, this method able to classify the fragments with a high accuracy. The algorithm was tested on several images of pottery fragments and yield results with accuracy as high as 86.51% of original grouped cases correctly classified.

Keywords:

Classification , feature extraction , GLCM , HSV color , texture,

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Competing interests

The authors have no competing interests.

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