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Batik Image Retrieval Based on Color Difference Histogram and Gray Level Co-Occurrence Matrix

Journal from gdlhub / 2016-11-14 04:27:04
Oleh : Agus Eko Minarno, Nanik Suciati, Telkomnika
Dibuat : 2014-09-01, dengan 1 file

Keyword : Batik, Image retrieval, Color difference histogram,Gray level co-occurrence matrix.
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/80

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.

Deskripsi Alternatif :

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.

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