Feature extraction Hue, Saturation, Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) for identification of woven fabric motifs in South Central Timor Regency

Lesiangi, F S; Mauko, Arfan Y; Djahi, Bertha S.

doi:10.1088/1742-6596/2017/1/012010

Cited by 13 publications

(11 citation statements)

References 3 publications

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“…Research was carried out to identify types of woven fabrics in TTS district using the HSV color feature extraction method, and GLCM texture characteristics, and to measure the similarity of woven fabrics using the Euclidean distance metho. The results obtained in this research obtained a GLCM texture accuracy level for color features of 55%, HSV color features of 62.5% and combination of color and texture features of 91.67% [10].…”

Section: Introductionmentioning

confidence: 67%

A Detection of Malacca Woven Fabric Motifs Using the YOLOv4 Method

Neno,

Yuniar Rahman,

Marisa

2024

bit-cs

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Malacca is one of the districts that has a weaving culture and also produces woven cloth in East NusaTenggara. The large number of types of woven cloth from each Malacca tribe means that outsiders andeven native Malacca people are not yet familiar with typical Malacca motifs, therefore a system isneeded that can help make it easier for people to recognize the types of woven fabric motifs. Malaccawoven fabric in this study was used to detect the types of woven fabric motifs in Malacca district usingthe YOLOv4 method. The results of detecting Malacca woven fabric motifs correspond to each type ofwoven fabric. Apart from that, the Malacca woven fabric motif detection system with YOLOv4technology is an effective and efficient solution in recognizing Malacca woven fabric motifs. Malaccawoven fabric is classified into four classes with an impressive mAP score of 100%.

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Section: Introductionmentioning

confidence: 67%

A Detection of Malacca Woven Fabric Motifs Using the YOLOv4 Method

Neno,

Yuniar Rahman,

Marisa

2024

bit-cs

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“…So, after obtaining the HSV matrix, the process carried out is quantizing the color histogram. This process is carried out to improve performance and reduce the computational burden of calculating image pixels [16]. A simple equation to get the HSV value is as follows:…”

Section: ) Geometric Invariant Moment: Geometric Invariantmentioning

confidence: 99%

“…Furthermore, from the integration of the GLCM feature extraction method and geometric feature extraction of a region of interest (ROI) for classifying tuna, it was found that the best classification accuracy was 86.76% obtained through the GLCM method [15]. In addition, the use of the HSV color feature extraction method and GLCM texture feature extraction to identify the type of woven fabric shows that the accuracy of the color and texture combination features is 91.67% [16]. Siar and Teshnehlab [17] used a combination of feature extraction to categorize tumor disease from digital images using the CNN method; the results obtained were very accurate, namely 99.7%, and increased when compared to using only one feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Latumakulita

Astawa

Mairi

et al. 2022

JOIV : Int. J. Inform. Visualization

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Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

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“…Agar dapat mempermudah dalam pengolahan citra, umumnya citra dengan ruang warna RGB akan diubah terlebih dahulu pada ruang warna Hue, Saturation dan Value atau HSV. Model warna HSV memberikan gambaran mengenai pewarnaan yang sama dengan intuisi manusia dalam menangkap warna [22]. Proses mengubah citra RGB menjadi citra HSV berguna untuk memudahkan proses segmentasi karena pada proses ini akan menghasilkan citra dengan warna HSV yang terlihat pada objek yang akan dilakukan segmentasi.…”

Section: Transformasi Citra Rgb Ke Hsvunclassified

Klasifikasi Citra Jenis Daun Berkhasiat Obat Menggunakan Algoritma Jaringan Syaraf Tiruan Extreme Learning Machine

Rumandan,

Nuraini,

Sadikin

et al. 2022

JoSYC

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Leaves are one part of a plant that has benefits for humans, especially for the health of the body. Leaves can be used as herbal medicine which can be an alternative that can help in increasing immunity and body resistance. However, not all leaves have medicinal properties, therefore knowledge of the types of medicinal leaves is important. The aim of this research is to develop a classification system for the image of medicinal leaves using the Extreme Learning Machine (ELM) artificial neural network model. To support the ELM algorithm, morphological feature extraction is used which can provide information about the shape characteristics of existing objects. Extreme Learning Machine (ELM) is also known as an artificial neural network approach that uses one hidden layer. At the classification stage, the Extreme Learning Machine (ELM) algorithm can determine the weight value between the input neurons and the hidden layer randomly so that the learning pattern becomes faster. Based on the results of the precision, recall and accuracy tests, the precision value is 90.67%, the recall value is 89.47% and accuracy of 90%. So, based on these results it can be said that the ELM model that was built can classify images of leaf types with medicinal properties well.

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Feature extraction Hue, Saturation, Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) for identification of woven fabric motifs in South Central Timor Regency

Cited by 13 publications

References 3 publications

A Detection of Malacca Woven Fabric Motifs Using the YOLOv4 Method

A Detection of Malacca Woven Fabric Motifs Using the YOLOv4 Method

Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Klasifikasi Citra Jenis Daun Berkhasiat Obat Menggunakan Algoritma Jaringan Syaraf Tiruan Extreme Learning Machine

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