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

Archaeology is the scientific study of the last remnants of humanitarian civilization (Son et al., 2013) and the reconstruction of fractures of ancient artifacts is important, as it helps archaeologists access to make inferences about past civilizations (Oxholm and Nishino, 2013). Therefore, over the last decade, there has been a trend toward the reconstruction of cultural heritage (Karianakis and Maragos, 2013; Youguang et al., 2013), which is considered among the difficult and unsolved problems in the field of computer vision (Papaodysseus et al., 2012; Rasheed and Nordin, 2014). Usually, pottery fragments are found in archaeological excavation sites, randomly mixed with each other. Consequently, classifying them manually is difficult and time consuming, because they commonly exceed thousands of fragments, (Belenguer and Vidal, 2012; Papaodysseus et al., 2012; Son et al., 2013). Therefore, numerous researches proposed various methods have good achievement to classify the fragments of archaeological pottery object by depending on two-dimensional image. For example, Ying and Gang (2010) focused on surface texture feature, (Smith et al., 2010; Zhou et al., 2011; Makridis and Daras, 2012) proposed approaches for the classification of ancient ceramic fragments by relying on color and texture. This study suggested a new approach rely on color and texture features that are extracted from each image after divided it into six parts, this way assists to obtain the most important features to achieve more accurate and higher results than the previous work. Thus, the main aims of this study achieve to:

This is a preparatory stage for the next phase, which is the reconstruction of the archaeological fragments with high accuracy. Therefore, in this study, we propose an algorithm to find a solution that classifies the archaeological fragments into groups, depending on their HSV color space and texture. In order to obtain the color features of an artifact, we consider Hue, Saturation and Value colors between its fragments. Also for the purpose of extracting the texture feature, we adopt the GLCM (Haralick et al., 1973) and calculate the Entropy, Contrast, Correlation and Homogeneity. Then we classify the fragments by using the KNN. 

System overview: This section is devoted to presenting the proposed method, which is divided into a set of steps, each one responsible for a specific job. As shown in Fig. 1, the proposed method can be represented by a series of steps, which has been programmed using MATLAB R2014 and performed on a standard laptop computer (Intel Core i5 2.5 GHz with 8 GB RAM). Image acquisition and pre-processing: This procedure is responsible for loading the JPG format image file into memory. This format is used in this study, because this image format is one of the most widely used nowadays, due to its simple format, easy usage and provides high image quality. The dimensions of each image used in this study are (300×210) pixels. Sometimes noisy objects with no meaning appear with different sizes and shapes, because the image is converted from RGB color to the HSV color. Consequently, we applied 2D median filtering (Huang and Yang, 1979) to remove noisy objects.

Feature extraction is an important component in pattern recognition, where the feature vector is a list of descriptions that include sufficient information to identify the pattern. In this study, the features were extracted from fragments depending on their color and texture, in order to select important features for recognition. HSV color feature extraction: Color value varies according to three main factors, these factors determine the color that humans are able to see. The color discrimination is based on three elements, namely, Hue,Saturation, Value (HSV) (Levkowitz, 1997). As shown in Fig. 2, the angle above the circle from the axis describe the Hue (H), which has the value a range (0- 360°). Saturation (S) is the distance from the axis,which is representing the vibrancy of the color, its value a range between 0 and 100°. Finally, the distance along the axis represents the Value (V), which is the brightness or intensity of the color. Its range is from 0-100°. In order to extract HSV as a cumulative histogram vector for each sub-block, we apply a procedure that is similar to the one used in Kavitha et al. (2011), who explain the proposed algorithm thoroughly. Their algorithm includes the following steps:

Step 1 : Divided each of Hue into eight parts,Saturation into three parts and intensity into three parts, depending on the human eye s ability to distinguish.