Content based Batik Image Retrieval

Content Based Batik Image Retrieval (CBBIR) is an area of research that focuses on image processing based on characteristic motifs of batik. Basically the image has a unique batik motif compared with other images. Its uniqueness lies in the characteristics possessed texture and shape, which has a unique and distinct characteristics compared with other image characteristics. To study this batik image must start from a preprocessing stage, in which all its color images must be removed with a grayscale process. Proceed with the feature extraction process taking motifs characteristic of every kind of batik using the method of edge detection. After getting the characteristic motifs seen visually, it will be calculated by using 4 texture characteristic function is the mean, energy, entropy and stadard deviation. Characteristic function will be added as needed. The results of the calculation of characteristic functions will be made more specific using the method of wavelet transform Daubechies type 2 and invariant moment. The result will be the index value of every type of batik. Because each motif there are the same but have different sizes, so any kind of motive would be divided into three sizes: Small, medium and large. The perfomance of Batik Image similarity using this method about 90-92%.

[2]  Dipankar Hazra Texture Recognition with combined GLCM,Wavelet and Rotated Wavelet Features , 2011 .

[3]  Prabir Kumar Biswas,et al.  Texture image retrieval using rotated wavelet filters , 2007, Pattern Recognit. Lett..

[4]  R. B. Bahaweres,et al.  Batik image retrieval based on similarity of shape and texture characteristics , 2012, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[5]  P. Anandhakumar,et al.  Neuro-Fuzzy Based Clustering Approach For Content Based Image Retrieval Using 2D- Wavelet Transform , 2009 .

[6]  Aniati Murni,et al.  Batik Image Reconstruction Based On Codebook and Keyblock Framework , 2009 .

[7]  B. Vanajakshi CLASSIFICATION OF BOUNDARY AND REGION SHAPES USING HU-MOMENT INVARIANTS , 2012 .

[8]  Marimin Marimin,et al.  Analisis Data Citra Buah Buahan Dengan Algoritma Fagin Dan Threshold , 2008 .

[9]  Ronald Fagin,et al.  Fuzzy queries in multimedia database systems , 1998, PODS '98.

[10]  Adi Sucipto Aji Fuzzy Rule-Based Sistem Temukembali Citra Bunga , 2007 .

[11]  Agus Harjoko,et al.  Analysis of image similarity with CBIR concept using wavelet transform and threshold algorithm , 2013, 2013 IEEE Symposium on Computers & Informatics (ISCI).

[12]  Benhard Sitohang,et al.  Algorithms of Clustering and Classifying Batik Images Based on Color, Contrast and Motif , 2005 .

[13]  Ronald Fagin,et al.  Combining Fuzzy Information from Multiple Systems , 1999, J. Comput. Syst. Sci..

[14]  Ying Liu,et al.  Region-based image retrieval with high-level semantics using decision tree learning , 2008, Pattern Recognit..