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     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
2015(Vol.10, Issue:12)
Article Information:

Using Both HSV Color and Texture Features to Classify Archaeological Fragments

Nada A. Rasheed and Md Jan Nordin
Corresponding Author:  Nada A. Rasheed 
Submitted: March ‎28, ‎2015
Accepted: ‎April ‎22, ‎2015
Published: August 25, 2015
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.

Key words:  Classification, Feature extraction, , feature extraction, GLCM, HSV color, texture, ,
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Cite this Reference:
Nada A. Rasheed and Md Jan Nordin, . Using Both HSV Color and Texture Features to Classify Archaeological Fragments. Research Journal of Applied Sciences, Engineering and Technology, (12): 1396-1403.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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