Image Texture Feature Extraction & Recognition of Chinese Herbal Medicine Based on Gray Level Co-Occurrence Matrix

2012 ◽  
Vol 605-607 ◽  
pp. 2240-2244 ◽  
Author(s):  
Qing Liu ◽  
Xi Ping Liu ◽  
Li Jun Zhang ◽  
Li Min Zhao
Keyword(s):  
Feature Extraction ◽  
Herbal Medicine ◽  
Chinese Herbal Medicine ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Gray Level ◽  
Inertia Moment ◽  
Chinese Herbal ◽  
Occurrence Matrix

In order to effectively extract Chinese herbal medicine (CHM) image feature information, and automatically identify the CHM images, a method of CHM image feature extraction and recognition based on gray level co-occurrence matrix (GLCM) is put forward. Firstly, on the basis of the acquired colour CHM image is converted to the gray-scale image, the four texture feature parameters, angular second moment (ASM)，inertia moment (IM)，entropy and correlation are extracted utilizing the GLCM, and then CHM image recognition is carried out by using those feature values with resistance geometric distortion. The experimental results show that the method of generating GLCM and extraction of image texture features can effectively identify the CHM image, which can bring significance to the modern recognition and identification of CHM.

scholarly journals Image Feature Extraction and Retrieval of the Euler Number to Chinese Herbal Medicine based on PCNN

2019 ◽  
Vol 1335 ◽  
pp. 012016
Author(s):  
Qing Liu ◽  
Xiaolong Zhao ◽  
Hongping Yang ◽  
Dongfeng Li ◽  
Weijun Ling ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Herbal Medicine ◽  
Chinese Herbal Medicine ◽  
Euler Number ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Chinese Herbal

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

scholarly journals Ekstraksi Fitur Citra Songket Berdasarkan Tekstur Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM)

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Ismi Amalia
Keyword(s):  
Feature Extraction ◽  
Data Collection ◽  
Texture Feature ◽  
Image Texture ◽  
Gray Level ◽  
Average Difference ◽  
Collection Data ◽  
Occurrence Matrix ◽  
Parameter Values

Abstrak— Songket merupakan warisan budaya Indonesia yang  harus dijaga dan dilestarikan. Pelestarian songket dapat dilakukan dengan pendataan secara komputerisasi. Pendataan dapat dilakukan dengan pengenalan pola motif songket. Dalam pengenalan pola, ekstraksi fitur merupakan hal yang penting untuk mendapatkan informasi citra digital. Informasi dari hasil ekstraksi fitur digunakan dalam proses klasifikasi. Penelitian ini akan mengekstraksi fitur citra songket Aceh. Ekstraksi fitur tekstur menggunakan metode Gray Level Co-Occurrence Matrix (GLCM). Hasil ekstraksi fitur dapat digunakan untuk pendataan citra songket Aceh serta juga dapat digunakan untuk klasifikasi motif songket Aceh dengan menggunakan Jaringan Syaraf Tiruan (JST). Pengumpulan data pada penelitian ini melalui observasi dan wawancara. Implementasi metode yang diusulkan menggunakan Matlab R2009a. Pengujian menggunakan lima sampel citra songket Aceh. Hasil penelitian ini adalah nilai-nilai parameter dari metode GLCM meliputi fitur entropy, sum average, difference entropy dan autocorrelation. Diharapkan fitur-fitur ini dapat digunakan untuk proses klasifikasi citra songket Aceh.Kata kunci— Ekstraksi fitur, Gray Level Co-Occurrence Matrix (GLCM), Jaringan Syarat Tiruan (JST), Songket Aceh. Abstract - Songket is an Indonesian cultural heritage that must be preserved and preserved. The preservation of songket can be done by computerizing data collection. Data collection can be done by introducing songket motif patterns. In pattern recognition, feature extraction is important for obtaining digital image information. Information from the results of feature extraction is used in the classification process. This study will extract the features of the Aceh songket image. Texture feature extraction using the Gray Level Co-Occurrence Matrix (GLCM) method. Feature extraction results can be used for data collection of Aceh songket images and can also be used for the classification of Aceh songket motifs using Artificial Neural Networks (ANN). Data collection in this study through observation and interviews. The implementation of the proposed method uses Matlab R2009a. The test uses five samples of Aceh songket images. The results of this study are the parameter values of the GLCM method including entropy features, sum average, difference entropy and autocorrelation. It is expected that these features can be used for the process of classification of Aceh songket images.Keywords - Feature extraction, Gray Level Co-Occurrence Matrix (GLCM), Artificial Condition Network (ANN), Aceh SongketKeywords -

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

scholarly journals PENGENALAN TAS RANSEL PADA CITRA DIGITAL DENGAN EKSTRAKSI FITUR TEKSTUR MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX

2018 ◽  
Vol 4 (4) ◽  
pp. 258
Author(s):  
Cahya Rahmad ◽  
Mungki Astiningrum ◽  
Ade Putra Lesmana
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Gray Level ◽  
Significant Development ◽  
Average Percentage ◽  
The Road ◽  
The Third ◽  
Last Stage ◽  
On The Road ◽  
Occurrence Matrix

The Backpack is one type of bag that experienced significant development. Many people buy it for their needs. However, when assessing a backpack directly or on the road, he could not recognize the backpack. The generally people want to buy backpacks must look at the price, color, shape, features, and the main ingredients of manufacture. Therefore, in image processing, there is a feature extraction theory for the process of recognizing an object. The backpack itself has a different texture. So that the introduction of the object is better done texture feature extraction with the gray level Co-occurrence matrix method. After that, then get the uniqueness of the backpack image to the classification with the image of the backpack in the database. The last stage in this study the authors conducted trials in 3 conditions. The first condition is based on a backpack photo-taking background. The second condition is based on the pixel capacity of the camera to retrieve the backpack image. And the third condition is based on the brightness of the backpack image. Of these three conditions, a percentage of matching values was obtained in the first condition with an average percentage of 90%, the second condition with an average percentage of 80% and last on the third condition with an average percentage of 70%.

scholarly journals Using Color and Texture Feature Extraction Technique to Retrieve Image

2019 ◽  
Vol 8 (11S) ◽  
pp. 915-917
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Color Images ◽  
Color Histogram ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Color Feature ◽  
Occurrence Matrix ◽  
Feature Extraction Technique

This paper proposes a content image retrieval using the texture and the color feature of the images. Although for extraction of texture feature, the “gray level co-occurrence matrix (GLCM) algorithm” is used and for extracting color feature the color histogram is used. The presented system is tested on the WANG database that contains a thousand color images with ten different classes by the help of three various type of distances

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