Content Based Batik Image Retrieval

Abstract

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%.

Classification of Batik Lamongan Based on Features of Color, Texture and Shape

“…Rangkuti et al [8] conducted research on content based batik image retrieval. The research is done using the features of texture by using wavelet and features shape by using moment invariant.…”

Texture Fusion for Batik Motif Retrieval System

“…This study applied edge feature orientation combined with micro structure descriptor for enhancing retrieval performance. Rangkuti, et al [10] reported using Canny edge detection to an input image, wavelets as texture features and invariant moment as shape features method. The performance results achieved optimal precision of average 90% -92%.…”

Image Analysis: Performance Gaps of Batik Craft in Yogyakarta, Indonesia

“…Basically, the depiction of the batik is a process of matching the characteristics of image search with characteristics (Rangkuti et al, 2014;and Hiremath & Pujari, 2007).…”

“…Some support functions in the craft motifs on the shape and texture with any characteristic value generated through the process of wavelet decomposition, including the gain coefficient approximation. The image of batik craft fabrics includes: Motif; Color; Ornament; Decomposition pattern recognition; Edge Detection; Grayscale; Feature Extraction; and Texture (Rangkuti, Harjoko, & Putro, 2014).…”