scholarly journals CBIR Penyakit Kulit dengan Metode Co-Occurrence Matrix dan Color Moments

2017 ◽  
pp. 62
Author(s):  
Ni Putu Chendy Widya Santi ◽  
I Ketut Gede Darma Putra ◽  
I Made Sunia Raharja
Keyword(s):  
Skin Disease ◽  
Color Space ◽  
Texture Feature ◽  
Image Texture ◽  
Research Use ◽  
Color Feature ◽  
Skin Tag ◽  
Moments Method ◽  
Similarity Computation ◽  
Occurrence Matrix

Content Based Image Retrieval (CBIR) is a technique for searching images from database based on information from the image which developed because the technique based on text-based is less effective for represent an image. CBIR skin disease in this research use 12 sample of skin disease images such as Acne, Acropustulosis, Alopecia, Dermatitis, Hemangioma, Herpes, Ichtyosis, Molluscum, Nummular, Skin Tag, Urticaria, and Vitiligo. Method use for this research is for extraction texture feature and color feature from a skin disease image. Texture feature is using co-occurrence Matrix which compute energy, contrast, entropy, homogeneity, and correlation until vector texture result. Extraction color use color moments to compute color space using three moments which result color feature from color distributions such as mean, standard deviation, and skewness. Final result showed the comparison of similarity computation of two methods is the acuration of Color Moments method is more robust than Co-occurrence Matrix Method for skin disease images.

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

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

Content Based Images Retrieval Based on HSV Color Space and GLCM

2014 ◽  
Vol 644-650 ◽  
pp. 4287-4290
Author(s):  
Ching Hun Su ◽  
Huang Sen Chiu ◽  
Tsai Ming Hsieh
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Color Space ◽  
Texture Feature ◽  
Hsv Color Space ◽  
Retrieval Scheme ◽  
Image Retrieval System ◽  
Retrieval Problem ◽  
Occurrence Matrix ◽  
Better Than

We propose a practical image retrieval scheme to retrieve images efficiently. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Gray Level Co-occurrence matrix to compare the images of database. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues of both the content based image retrieval system and a text based image retrieval system. Experimental results reveal that proposed scheme is better than the conventional methodologies.

RELATIONSHIP BETWEEN SOIL MOISTURE CONTENT IN PADDY FIELD AND ITS IMAGE TEXTURE

2016 ◽  
Vol 78 (1-2) ◽  
Author(s):  
Siti Khairunniza Bejo ◽  
Nor Hafizah Sumgap ◽  
Siti Nurul Afiah Mohd Johari
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Color Space ◽  
Texture Features ◽  
Image Texture ◽  
Gray Level ◽  
Second Moment ◽  
Variance Homogeneity ◽  
Occurrence Matrix

The aim of this study is to identify the relationship between soil moisture content and its image texture. Soil image was captured and converted into CIELUV color space. These images were later used to develop two dimensional gray level co-occurrence matrix. Eight texture features extracted from gray level co-occurrence matrix namely mean, variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation was used for the analysis. The results has shown that the image texture properties can be used to relate with soil moisture content, where variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation gave significant responds to the moisture content. The highest value of correlation was gathered from entropy with r = -0.522.

Research on the Application of Pattern Recognition in the Intelligent Identification of Wallpaper Labeling

2011 ◽  
Vol 65 ◽  
pp. 518-523
Author(s):  
Ye Qin Wang ◽  
Yu Qing Wang ◽  
Liang Hai Chen
Keyword(s):  
Simulated Annealing Algorithm ◽  
Recognition Rate ◽  
Color Space ◽  
Texture Feature ◽  
Feature Space ◽  
Feature Parameters ◽  
Mode Characteristic ◽  
Rgb Color Space ◽  
Annealing Algorithm ◽  
Occurrence Matrix

In order to realize the intelligent identification of wallpaper labeling, the wallpaper texture and the characteristic of color are comprehensively considered in this paper to rich the pattern feature space. Firstly, the suitable GLCM (gray level-gradient co-occurrence matrix)is constructed to describe the texture feature and extract the feature parameters; And the parameters of color low order matrix are picked up in the RGB color space to constitute mode characteristic vector together. Secondly, to reduce the computation and improve the describing abilities, the Simulated Annealing Algorithm is applied to select feature value from 17 feature parameters. Lastly, the integrated classifier of BP neural network is designed to achieve 94.03% overall recognition rate, which is higher than the traditional one. The experiment results also have shown that this method is effective.

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 -

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.

MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS FOR IMAGE TEXTURE FEATURE REPRESENTATION

2014 ◽  
Vol 28 (03) ◽  
pp. 1455005 ◽  
Author(s):  
FANG WANG ◽  
ZONG-SHOU LI ◽  
GUI-PING LIAO
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Noise Immunity ◽  
Texture Feature ◽  
Feature Representation ◽  
Image Texture ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Image Blurring ◽  
Occurrence Matrix ◽  
Detrended Fluctuation

Multifractal theory has been widely used in different kinds of fields. In this paper, methods were proposed to extract two kinds of multifractal descriptors of gray series and two-dimensional surfaces for gray image based on the multifractal detrended fluctuation analysis. The proposed multifractal parameters can be well described by texture feature through the test of some textures. Three aspects of experiments have been conducted to verify the robustness of the proposed parameters, which include noise immunity, degree of image blurring and compression ratio. Comparisons were conducted between the proposed parameters and other kinds of texture feature parameters calculated by the standard multifractal analysis, the method of differential box counting and the methods of gray level co-occurrence matrix. Results demonstrate that the proposed exponents of H(2) and h(2) have great noise immunity and are robust to image compression and blurring.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

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