An Efficient Batik Image Retrieval System Based on Color and Texture Features

Research in batik image retrieval is still challenging today. In this paper, we present an efficient system for batik image retrieval that combine color and texture features. The proposed approach is based on color auto-correlogram method as color feature extraction method and Gray Level Co-occurrence Matrix (GLCM) method as texture feature extraction method. Firstly, HSV (Hue Saturation Value) color space but only the hue component is quantized into 18 color categories and continued in the process of color autocorrelogram. Secondly, converts the batik image from RGB color space to grayscale to obtain texture features using GLCM (Gray Level Co-occurrence Matrix). Finally, the integration of the color and texture features provide a robust feature set for batik image retrieval with calculates the similarity measure. Precision and recall are used as the accuracy measure in our proposed research, then the results are compared with the well-known existing approaches. The experimental results show that the proposed batik image retrieval is more accurate and efficient in retrieving the user interested images.