Classification of Sambas Traditional Fabric “Kain Lunggi” Using Texture Feature

Siregar, Alda Cendekia; Octariadi, Barry Ceasar

doi:10.22146/ijccs.49782

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The image was sourced from [4] To date, a significant amount of research had been done on the robust detection, description and matching of invariant features related to motif and pattern classification. Features extraction algorithm and classification methods were applied to batik motif and batik making using convolutional neural network (CNN) model architectures [6]- [10], using multiwindow and multiscale extended center symmetric local binary patterns (MU2ECS-LBP) [11] and [12], using scale invariant feature transform (SIFT) [13], [14] using gray level co-occurrence matrices (GLCM) [15], [16], fine arts [17]- [19], for license plate recognition [20], [21], and tattoo recognition [22]- [24]. Even though the reported performance was quite high but these methods still suffer from false positives due to similar features that contain more than one pattern and noisy background.…”

Section: Introductionmentioning

confidence: 99%

Iban plaited mat motif classification with adaptive smoothing

Joseph

Hipiny

Ujir

2023

IJ-AI

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<span lang="EN-US">Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using binary robust invariant scalable keypoints (BRISK) features with random sample consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: scale+rotation, viewpoint, image blur, joint photographic experts group (JPEG) compression, scale and illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG compression, illumination, and viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline.</span>

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Section: Introductionmentioning

confidence: 99%

Iban plaited mat motif classification with adaptive smoothing

Joseph

Hipiny

Ujir

2023

IJ-AI

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“…Songket has varied motifs, and almost every songket motif from various regions has the same motif. However, the songket motifs from multiple areas are different [6], [7]. The application can identify various types of Lombok songket motifs.…”

Section: Introductionmentioning

confidence: 99%

Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Sasongko

Jayanti²,

Ariessaputra³

2022

JOIV : Int. J. Inform. Visualization

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Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the system’s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90° angle is 68.33 percent.

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“…However, several previous studies related to the classification of Batik based on image processing have been developed. This study consisted of three main processes: pre-processing, feature extraction, and classification, as in [3]- [9]. Scaling and rotating of images are performed by pre-processing, followed by feature extraction using the multiwindow and multiscale extended center symmetric local binary patterns (MU2ECS-LBP) algorithms.…”

mentioning

confidence: 99%

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Winarno

Hadikurniawati

Septiarini

et al. 2022

Int. J. Adv. Intell. Informatics

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Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.

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Classification of Sambas Traditional Fabric “Kain Lunggi” Using Texture Feature

Cited by 6 publications

References 3 publications

Iban plaited mat motif classification with adaptive smoothing

Iban plaited mat motif classification with adaptive smoothing

Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Analysis of color features performance using support vector machine with multi-kernel for batik classification

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