scholarly journals Color moment and gray level co-occurrence matrix in classification of soil organic matter for patchouli plantation

2020 ◽  
Vol 19 (2) ◽  
pp. 983
Author(s):  
Candra Dewi ◽  
Akbar Grahadhuita ◽  
Lailil Muflikhah
Keyword(s):  
Organic Matter ◽  
Nearest Neighbor ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Occurrence Matrix ◽  
Selection Of ◽  
Color And Texture Features

<span>Patchouli is one of the essential plants that have the most potential and widely cultivated in Indonesia. Patchouli is greedily absorbing soil nutrients and organic matter. Therefore, the selection of soil with high organic matter will maximize the patchouli’s productivity. This paper aims to facilitate soil’s organic matter identification by classifying soil image based on the combination of color and texture features. The color feature extraction was done using the Color Moments method and the texture feature was done using Gray Level Co-occurrence Matrix (GLCM) method. The selection of features was performed to obtain the best combination of color and texture features. The selected features then was used as input of classification by using Modified K-Nearest Neighbor (MKNN). The samples of soil that used as data were taken from several districts in Blitar, East Java province. The testing result of this research showed the highest accuracy of 93,33% by using 180 training data, and also particular color and texture feature combination.</span>

scholarly journals Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network

2019 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Candra Dewi ◽  
Suci Sundari ◽  
Mardji Mardji
Keyword(s):  
Neural Network ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Color Features ◽  
Accuracy Result ◽  
Occurrence Matrix

Patchouli (Pogostemon Cablin Bent) has higher PA (Patchouli Alcohol) and oil production if grown in soil containing 75% organic matter. One way that can be used to detect the content of organic matter is to use soil images. The problem in the use of soil images is the color of the soil that is almost similar, namely the gradation between dark brown to black. Therefore, color features are not enough to be used as input in the recognition process. For this purposes, texture features are added in this study in addition to color features. The color features are extracted using color moment and the texture features are extracted using Gray Level Co-occurrence Matrix (GLCM). These feature was then chosen to get the best combination as input in the identification process using the Backpropagation Neural Network (BPNN). The system identifies the quantity of soil organic matter into five classes, namely very low, low, medium, high, and very high. The highest accuracy result obtained was 73% and MSE value 0.5122 by using five GLCM features (Angular Second Moment, contrast, correlation, Inverse Difference Moment, and entropy). This result was obtained by using the BPNN parameter, namely learning rate values 0.5, maximum iteration values of 1000, number training data 210, and total test data 12.

scholarly journals Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-119
Author(s):  
Daurat Sinaga ◽  
Feri Agustina ◽  
Noor Ageng Setiyanto ◽  
Suprayogi Suprayogi ◽  
Cahaya Jatmoko
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Correlation Energy ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Testing Data ◽  
Occurrence Matrix

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

scholarly journals A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

2017 ◽  
Vol 7 (5) ◽  
pp. 2502 ◽  
Author(s):  
G. S. N. Murthy ◽  
Srininvasa Rao. V ◽  
T. Veerraju
Keyword(s):  
Nearest Neighbor ◽  
Texture Classification ◽  
Texture Features ◽  
Significant Feature ◽  
Gray Level ◽  
Texture Image ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Novel Approach ◽  
Occurrence Matrix

The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually.  In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages.  In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff.  In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts.  In stage 3, Co-occurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification.  Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features.  To prove the efficiency of the proposed method, it is tested on different stone texture image databases.  The proposed method resulted in high classification rate when compared with the other existing methods.

scholarly journals Pengenalan Jenis Buah pada Citra Menggunakan Pendekatan Klasifikasi Berdasarkan Fitur Warna Lab dan Tekstur Co- Occurrence

Jurnal INFORM ◽  
2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Evy Kamilah Ratnasari
Keyword(s):  
Feature Recognition ◽  
Nearest Neighbor ◽  
Fruit Size ◽  
Texture Features ◽  
Real Data ◽  
K Nearest Neighbor ◽  
Education Industry ◽  
Basic Features ◽  
Computer Recognition ◽  
Color And Texture Features

Abstract — Fruit recognition can be automatically applied to the field of education, industry, sales, as well as science. In the vision of computer recognition of fruit relies on four basic features that describe the characteristics of the fruit, i.e., size, color, shape, and texture. The fruit recognition through the RGB image results of cameras using the features of shape and size are not reliable and effective, because in a real data image can be composed of several different sizes of fruit on each type of fruit so it can't be identified morphologically the fruit size and uniformity that can affect the results of the classification. This journal based on the feature recognition method of building colors and textures for the classification of fruit.The classification is done by K-Nearest Neighbor based on color and texture features co-occurrence. Experimental results of 1882 dataset image of fruit for 12 different classes can recognize the fruit in both color and texture features based with the highest accuracy of 92%.

NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi142-vi142
Author(s):  
Kaylie Cullison ◽  
Garrett Simpson ◽  
Danilo Maziero ◽  
Kolton Jones ◽  
Radka Stoyanova ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Tumor Growth ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Difference Matrix ◽  
Patient Response ◽  
Significant Difference ◽  
Occurrence Matrix ◽  
Surrounding Areas

Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.

DIAGNOSIS OF LUNG NODULES FROM 2D COMPUTER TOMOGRAPHY SCANS

2020 ◽  
Vol 32 (03) ◽  
pp. 2050017
Author(s):  
Ayat Karrar ◽  
Mai S. Mabrouk ◽  
Manal AbdEl Wahed
Keyword(s):  
Nearest Neighbor ◽  
Survival Rates ◽  
Maximum Intensity Projection ◽  
Lung Nodules ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Cad System ◽  
Different Shapes ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Cancers typically are both highly dangerous and common. Among these, lung cancer has one of the lowest survival rates compared to other cancers. CT scans can reveal dense masses of different shapes and sizes; in the lungs, these are called lung nodules. This study applied a computer-aided diagnosis (CAD) system to detect candidate nodules — and diagnose it either solitary or juxtapleural — with equivalent diameters, ranging from 7.78[Formula: see text]mm to 22.48[Formula: see text]mm in a 2D CT slice. Pre-processing and segmentation is a very important step to segment and enhance the CT image. A segmentation and enhancement algorithm is achieved using bilateral filtering, Thresholding the gray-level transformation function, Bounding box and maximum intensity projection. Border artifacts are removed by clearing the lung border, erosion, dilation and superimposing. Feature extraction is done by extracting 20 gray-level co-occurrence matrix features from four directions: [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] and one distance of separation ([Formula: see text] pixel). In the classification step, two classifiers are proposed to classify two types of nodules based on their locations: as juxtapleural or solitary nodules. The two classifiers are a deep learning convolutional neural network (CNN) and the K-nearest neighbor (KNN) algorithm. Random oversampling and 10-fold cross-validation are used to improve the results. In our CAD system, the highest accuracy and sensitivity rates achieved by the CNN were 96% and 95%, respectively, for solitary nodule detection. The highest accuracy and sensitivity rates achieved by the KNN model were 93.8% and 96.7%, respectively, and K was set to 1 to detect juxtapleural nodules.

scholarly journals Deteksi Batik Parang Menggunakan Fitur Co-Occurence Matrix Dan Geometric Moment Invariant Dengan Klasifikasi KNN

2016 ◽  
pp. 40
Author(s):  
Ni Luh Wiwik Sri Rahayu Ginantra
Keyword(s):  
Matrix Method ◽  
Nearest Neighbor ◽  
Texture Features ◽  
Sufficient Information ◽  
K Nearest Neighbor ◽  
Moment Invariant ◽  
The Public ◽  
Geometric Moment ◽  
Visual Identification ◽  
Occurrence Matrix

Batik motifs are the base or the blueprint of batik patterns which serve as the core of the batik image design, and therefore the meaning of a sign, symbol or logo in a batik work can be revealed through its motifs. Visual identification requires visual skills and knowledge in classifying patterns formed in a batik image. Lack of media providing information on batik motifs makes the public unable to have sufficient information about batik motifs. Looking at this phenomenon, this study is conducted in order to perform visual identification using a computer that can assist and facilitate in identifying the types of batik. The methods used for batik image recognition are the Co-occurrence Matrix method to provide extraction of batik texture features, and the Geometric Moment Invariant method, while K Nearest Neighbor is used to classify batik images. The results on the accuracy values obtained reveal that the of 80%, compared to the accuracy value result using the Co-occurrence Matrix method that is 70%.  

scholarly journals Classification of Computer Graphic Images and Photographic Images Based on Fusion of Color and Texture Features

2021 ◽  
Vol 35 (3) ◽  
pp. 201-207
Author(s):  
Halaguru Basavarajappa Basanth Kumar ◽  
Haranahalli Rajanna Chennamma
Keyword(s):  
Computer Graphic ◽  
Texture Feature ◽  
Texture Features ◽  
Digital Cameras ◽  
Global Features ◽  
Photographic Images ◽  
Occurrence Matrix ◽  
Paper Classification ◽  
Color And Texture Features

With the rapid advancement in digital image rendering techniques, allows the user to create surrealistic computer graphic (CG) images which are hard to distinguish from photographs captured by digital cameras. In this paper, classification of CG images and photographic (PG) images based on fusion of global features is presented. Color and texture of an image represents global features. Texture feature descriptors such as gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) are considered. Different combinations of these global features are investigated on various datasets. Experimental results show that, fusion of color and texture features subset can achieve best classification results over other feature combinations.

scholarly journals DCT Untuk Ekstraksi Fitur Berbasis GLCM Pada Identifikasi Batik Menggunakan K-NN

2021 ◽  
Vol 3 (1) ◽  
pp. 1-6
Author(s):  
Zulfrianto Yusrin Lamasigi
Keyword(s):  
Feature Extraction ◽  
Discrete Cosine Transform ◽  
Confusion Matrix ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Cosine Transform ◽  
A Value ◽  
Testing Data ◽  
Occurrence Matrix

Batik merupakan kain yang dibuat khusus, batik sendiri terbilang unik karena memiliki motif tertentu yang dibuat berdasarkan unsur budaya dari daerah asal batik itu dibuat. setiap motif dan warna batik berbeda-beda sehingga sulit untuk dikenali asal dari motir batik itu sendiri. penelitian ini bertujuan untuk meningkatkan hasil ektraksi fitur pada identifikasi motif batik. metode yang digunakan dalam penelitian ini adalah Discrete Cosine Transform bertujuan untuk meningkatkan hasil ektraksi fitur Gray Level Co-Occurrence Matrix untuk mendapatkan hasil akurasi identifikasi motif batik yang lebih baik, sedangkan untuk mengetahui nilai kedekatan antara data training dengan data testing citra batik akan menggunakan K-Nearest Neighbour berdasarkan nilai ekstraksi fitur yang diperoleh. dalam eksperimen ini dilakukan 4 kali percobaan berdasarkan sudut 0°, 45°, 90°, dan 135° pada nilai k=1, 3, 5, 7, dan 9. sementara itu, untuk menghitung tingkat akurasi dari klasifikasi KNN akan menggunakan confusion matrix. Dari uji coba yang di lakukan dengan menggunakan jumalah data training sebanyak 602 citra dan data testing 344 citra terhadap semua kelas berdasarkan sudut 0°, 45°, 90°, dan 135° pada nilai k=1, 3, 5, , dan 9 akurasi tertinggi yang diperoleh DCT-GLCM ada pada sudut 135° dengan nilai k=3 sebesar 84,88% dan yang paling rendah ada pada sudut 0° dengan nilai k=7 dan 9 sebesar 41,86%. Sedangkan hasil uji dengan hanya mennggunakan GLCM akurasi tertinggi ada pada sudut 135° dengan nilai k=1 sebesar 77,90% dan yang paling rendah ada pada sudut 90° dengan nilai k=7 sebesar 40,69%. Dari hasil uji coba yang dilakukan menunjukkan bahwah DCT bekerja dengan baik untuk meningkatkan hasil ekstraksi fitur GLCM yang dibuktikan dengan hasil rata-rata akurasi yang diperoleh.Batik is a specially made cloth, batik itself is unique because it has certain motifs that are made based on cultural elements from the area where the batik was made. each batik motif and color is different so it is difficult to identify the origin of the batik motir itself. This study aims to improve the feature extraction results in the identification of batik motifs. The method used in this research is Discrete Cosine Transform, which aims to increase the extraction of the Gray Level Co-Occurrence Matrix feature to obtain better accuracy results for identification of batik motifs, while to determine the closeness value between training data and batik image testing data will use K- Nearest Neighbor based on the feature extraction value obtained. In this experiment, 4 experiments were carried out based on angles of 0 °, 45 °, 90 °, and 135 ° at values of k = 1, 3, 5, 7, and 9. Meanwhile, to calculate the level of accuracy of the KNN classification, confusion matrix will be used. . From the trials carried out using the total training data of 602 images and testing data of 344 images for all classes based on angles of 0 °, 45 °, 90 °, and 135 ° at values of k = 1, 3, 5, and 9 accuracy The highest obtained by DCT-GLCM was at an angle of 135 ° with a value of k = 3 of 84.88% and the lowest was at an angle of 0 ° with values of k = 7 and 9 of 41.86%. While the test results using only GLCM, the highest accuracy is at an angle of 135 ° with a value of k = 1 of 77.90% and the lowest is at an angle of 90 ° with a value of k = 7 of 40.69%. From the results of the trials conducted, it shows that the DCT works well to improve the results of the GLCM feature extraction as evidenced by the average accuracy results obtained.

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