Nanik Suciati

Batik Motif Classification Using Color-Texture-Based Feature Extraction and Backpropagation Neural Network

Batik is an Indonesians traditional cloth which has been recognized as one of the world cultural heritage. Currently, there are hundreds of different batik motif which can be classified into 7 groups, i.e. Parang, Ceplok, Lereng, Megamendung, Semen, Lunglungan, and Buketan. This research develops a software to automatically identify motifs of batik image using color-texture-based feature extraction and backpropagation neural network. Color and texture features of batik image is extracted using combination of Color Co-occurence Matrix, Different Between Pixels of Scan Pattern, and Color Histogram for K-Means methods. The extracted features vectors are furthermore classified into motifs using Backpropagation Neural Network. The experiment shows that the software can recognize batik motifs quite well, with rate of Tanimoto Distance 0,37.

Batik classification using neural network with gray level co-occurence matrix and statistical color feature extraction

Indonesians Batik is one of culture heritage that recognized around the world. Batik has many variations of motif based on their region. This paper discusses feature extraction methods for classifying batik motifs in digital images. A single feature extraction method may result feature vector that is similar for two different images. In this research, the using of Gray Level Co-occurence Matrix (GLCM) and statistical color RGB features can represent more characteristics in extracting batik images. The extracted features vectors are furthermore classified into motifs using Backpropagation Neural Network with several scenarios for testing the level of accuracy. Some experiment by using single feature and combination of GLCM and statistical color RGB features show that the best result for classifying batik image is the combination of feature extraction with rate of precision 90.66%, recall 94% and accuracy 94%.

Batik image retrieval based on enhanced micro-structure descriptor

This paper describes a novel method for extracting features of batik images. This method is called enhanced micro-structure descriptor (EMSD). EMSD is the enhanced model of micro-structure descriptor (MSD) which proposed by Guang-Hai Liu. Different with MSD that uses only edge orientation similarity for creating micro-structure map and then utilises this map along with color values; EMSD adds a new micro-structure map that is based on color similarity and then utilises this map along with edge orientation values. The combination of MSD and the additional micro-structure descriptor is used as feature extractor in EMSD. This method is tested on 300 batik images, Corel datasets with 5,000 images and 10,000 images. We also compared EMSD to MSD and multi-textons histogram (MTH), which EMSD performance is superior than the other two.

Classification of textile image using support vector machine with textural feature

Fabrics have different materials, colors, and texture. The development of fashion is influenced by climate and fashion trends. In the particular climate of the summer for example, a floral pattern clothes are interesting and in other climates, the other fashion patterns, such as lines, polka dot, and strips might be more interesting. This study focuses on textile image classification based on its texture. Each texture in textile image has a particular characteristic that can distinguish with other motifs. The feature extraction method used in this study are Gray Level Co-occurrence Matrix (GLCM), Linear Binary Pattern (LBP), and a Moment Invariant (MI). Furthermore, all texture feature are then reduced using Principal Component Analysis (PCA). The experiment shows that the best result can be achieved using combination of GLCM and LBP features with accuracy 74.15% using linear kernel SVM.

Color correction using improved linear regression algorithm

Color correction is one of essential stages in image processing, which plays an important role during image acquisition or pre-processing to produce a better color quality, before being used in further process. This paper proposes a new method for color correction using an improved linear regression algorithm based on a stepwise model. This proposed method is designed for assessing a series of discrete color levels, for instance in a leaf color chart. Color chart as a reference image is used for controlling color levels of a captured image or calibrating the image sensor. The experiment is conducted in L*a*b* color space, therefore a transformation from RGB into L*a*b* is needed at the first phase. The best matched color level between reference and captured image will be selected by k-Means clustering method. Chosen color levels are used for constructing linear regression function. This function is applied as well for removing outlier among color levels. To ensure the result of this color correction does not depend on lighting condition, the color constancy algorithm is acquired. Gray World and White Patch are chosen for color constancy methods. Compared to ordinary linear regression and color correction without adding color constancy, the combination of Gray World and improved linear regression algorithm based on stepwise model shows the best result in almost entire datasets in various lighting conditions.

Fast discrete curvelet transform and HSV color features for batik image clansificotlon

Batik is one of the cultural heritages in Indonesia. Batik has many types spread around Indonesia. Related to the diversity of batik, an effort to develop a database to preserve batik information is required. Searching batik information from the database by using keywords such as the province name where a batik came from, sometimes is difficult. In some cases, people only has a batik image without knowing any additional information, such as motif name and its origin. Attaching a modul to classify batik image automatically into the database will be very useful, so that people can search more information about batik by inputting a batik image. This research proposes batik image classification using Fast Discrete Curvelet Transform (FDCT) and Hue Saturation Value (HSV) space as the representation of texture and color features, and K Nearest Neighbour (KNN) as the classifier. The experiment give a good result, which is showed by the worst classification error rate 3.33% for combined features vector.

Fractal-based texture and HSV color features for fabric image retrieval

Wide range of products such as clothing, bed linen, curtains, and shoes, use fabrics as main raw material. Fabrics have various types of materials, colors and patterns. Harmony in combining the various types of fabrics will affect the beauty of the resulted product. A system that can be used to retrieve some fabrics similar to a fabrics sample automatically will facilitate the combining process in creating a product. In this study, a fabrics image retrieval system using combination of fractal-based texture feature and HSV color feature is developed. The Canberra Distance is used to measure similarity between features vectors. The experiment which is done using two kinds of fabrics image datasets, i.e. “batik” and “common”, gives average recall 94% and 92%, respectively.

Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm

A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73% compares with the manual counting.

Enhancing tomato clustering evaluation using color correction with improved linear regression in preprocessing phase

Color inconsistency poses many difficulties when capturing the same object using different image capture devices. Color is one of main parts in image preprocessing and therefore color correction is needed to calibrate images in order to produce consistent color values. In this paper, we propose a new color correction method by employing combined linear regression with stepwise model to enhance the quality of tomatoes ripeness clustering. Macbeth ColorChecker is needed as a reference image while a test image to be corrected is captured by an Android smartphone camera. There are 12 color levels to be compared between reference and test image. However, only a number of color levels are selected by k-means clustering. The selected color levels are utilized to build a linear regression algorithm with stepwise model. The result confirms that color correction and color constancy increase the clustering performance by 10% up to 40% for all possible configurations.

Spline and color representation for batik design modification

A computer based system for batik design modification using spline and color representation is proposed to create a new batik design from the existing batik design image. The system can be used to support artisan/designer works for drawing motifs and applying color composition, so the design can be seen visually on the screen without having to make a real design. To evaluate the performance of the system, five batik images are used as the input of the system. The experimental results show that the system generates the similar batik design using spline and color representation from the existing batik design image and the user can edit the spline and its color to create new batik design.