Batik image retrieval based on color difference histogram and gray level co-occurrence matrix

Study in batik image retrieval is still challenging today. One of the methods for this problem is using a Color Difference Histogram (CDH), which is based on the difference of color features and edge orientation features. However, CDH is only utilising local features instead of global features; consequently it cannot represent images globally. We suggest that by adding global features for batik image retrieval, precision will increase. Therefore, in this study, we combine the use of modified CDH to define local features and the use of Grey Level Co-occurrence Matrix (GLCM) to define global features. The modified CDH is performed by changing the size of image quantisation, so it can reduce the number of features. Features that are detected by GLCM are energy, entropy, contrast and correlation. In this study, we use 300 batik images which consist of 50 classes and six images in each class. The experiment result shows that the proposed method is able to raise 96.5% of the precision rate which is 3.5% higher than the use of CDH only. The proposed method is extracting a smaller number of features; however it performs better for batik image retrieval. This indicates that the use of GLCM is effective combined with CDH.