Batik Image Retrieval Based on Color Difference Histogram and Gray Level Co-Occurrence Matrix
Abstract: Study in batik
images retrieval is still challenging until today. One of the methods for this
problem is using Color Difference Histogram (CDH) which is based on the
difference of color features and edge orientation features. However, CDH is
only utilizing local features instead of global features; consequently it
cannot represent images globally. We suggest that by adding global features for
batik images retrieval, the precision will increase. Therefore, in this study,
we combine the use of modified CDH to define local features and the use of Gray
Level Co-occurrence Matrix (GLCM) to define global features. The modified CDH
is performed by changing the size of image quantization, so it can reduce the
number of features. Features that detected by GLCM are energy, entropy,
contrast and correlation. In this study, we use 300 batik images which are
consisted of 50 classes and 6 images in each class. The experiment result shows
that the proposed method is able to raise 96.5% of 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 images retrieval. This
indicates that the use of GLCM is effective combined with CDH.
Author: Agus Eko Minarno,
Nanik Suciati
Journal Code: jptkomputergg140078
