FEATURE FUSION TECHNIQUE FOR COLOUR TEXTURE CLASSIFICATION SYSTEM BASED ON GRAY LEVEL CO-OCCURRENCE MATRIX

Internal density of the breast is a parameter that clearly affects the performance of segmentation and classification algorithms to define abnormality regions. Recent studies have shown that their sensitivity is significantly decreased as the density of the breast is increased. In this chapter, enhancement and segmentation process is applied to increase the computation and focus on mammographic parenchyma. This parenchyma is analyzed to discriminate tissue density according to BIRADS using Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM), Fractal Dimension (FD), and feature fusion technique is applied to maximize and enhance the performance of the classifier rate. The different methods for computing tissue density parameter are reviewed, and the authors also present and exhaustively evaluate algorithms using computer vision techniques. The experimental results based on confusion matrix and kappa coefficient show a higher accuracy is obtained by automatic agreement classification.

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Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes

Interpretation ◽

10.1190/int-2014-0099.1 ◽

2015 ◽

Vol 3 (1) ◽

pp. T13-T23 ◽

Cited By ~ 8

Author(s):

Christoph Georg Eichkitz ◽

Marcellus Gregor Schreilechner ◽

Paul de Groot ◽

Johannes Amtmann

Keyword(s):

Seismic Anisotropy ◽

Texture Classification ◽

Spatial Arrangement ◽

Training Image ◽

Seismic Facies ◽

Gray Level ◽

Vienna Basin ◽

Neighboring Pixel ◽

Analysis Window ◽

Occurrence Matrix

Texture attributes describe the spatial arrangement of neighboring amplitudes values within a given analysis window. We chose a statistical texture classification method, the gray-level co-occurrence matrix (GLCM), and its derived attributes, to produce a semiautomated description of the spatial arrangement of seismic facies. The GLCM is a measure of how often different combinations of neighboring pixel values occur. We tested the application of directional GLCM-based attributes for the detection of seismic variability within paleoriver features. Calculation of 3D GLCM-based attributes can be done in 13 space directions. The results of GLCM-based attribute calculation differed depending on the chosen GLCM parameters (number of gray levels, analysis window, and direction of calculation). We specifically focused on how the direction of calculation influenced the computation of attributes, while keeping other parameters constant. We first tested the workflow on a 2D training image and later ran on a real seismic amplitude volume from the Vienna Basin. Based on the GLCM-based attributes, we could map the channel features and extract them as geobodies. Additionally, we generated a new set of directional GLCM-based attributes to detect spatial changes in the seismic facies. By comparing these directional attributes, we could determine areas within the channel features having higher directional variability. Areas with higher tendency to directional variations might be associated with changes in lithology, seismic facies, or with seismic anisotropy.

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Combining gray-level co-occurrence matrix and statistics features for rotation invariant texture classification in wavelet domain

2015 8th International Congress on Image and Signal Processing (CISP) ◽

10.1109/cisp.2015.7407938 ◽

2015 ◽

Cited By ~ 1

Author(s):

Li Liu ◽

Longfei Yang ◽

Yizheng Wang ◽

Aiqi Yang

Keyword(s):

Texture Classification ◽

Gray Level ◽

Wavelet Domain ◽

Rotation Invariant ◽

Occurrence Matrix

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Digital Image Technology Mineral Feature Extraction Method Research

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.3906 ◽

2014 ◽

Vol 989-994 ◽

pp. 3906-3909

Author(s):

Jian Peng ◽

Dong Bo Li

Keyword(s):

Feature Extraction ◽

Traditional Method ◽

Extraction Method ◽

Texture Classification ◽

Gabor Wavelet ◽

Gabor Transform ◽

Support Vector ◽

Gray Level ◽

Feature Extraction Method ◽

Occurrence Matrix

This paper presents a texture classification algorithm using Gabor wavelet and Gray Level Co-occurrence Matrix as feature extraction method and Support Vector Machine as classifier. Gabor transform and Gray Level Co-occurrence Matrix are used to get the features of the digital images, SVM classifiers are followed to build image and realize classification. The results of the experiments have shown that the methods described in this paper can improve the rate of correct classification effectively than traditional method of classification.

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Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features

Sensors ◽

10.3390/s21217408 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7408

Author(s):

Smita Khade ◽

Shilpa Gite ◽

Sudeep D. Thepade ◽

Biswajeet Pradhan ◽

Abdullah Alamri

Keyword(s):

Contact Lenses ◽

Feature Fusion ◽

Local Level ◽

Physical Space ◽

Support Vector ◽

Gray Level ◽

Block Truncation Coding ◽

Liveness Detection ◽

Occurrence Matrix ◽

Fusion Approach

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.

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

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i5.pp2502-2513 ◽

2017 ◽

Vol 7 (5) ◽

pp. 2502 ◽

Cited By ~ 1

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.

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Research on Skin Texture Classification by Gray Level Co-occurrence Matrix and the BP Neural Network

Proceedings of the 2015 International Conference on Test, Measurement and Computational Methods ◽

10.2991/tmcm-15.2015.8 ◽

2015 ◽

Author(s):

Qiaohua Liu ◽

Tianhua Chen ◽

Xiaoyi Wang ◽

Jiping Xu ◽

Li Wang ◽

...

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Texture Classification ◽

Gray Level ◽

Skin Texture ◽

Occurrence Matrix

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Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

Sensors ◽

10.3390/s21010212 ◽

2020 ◽

Vol 21 (1) ◽

pp. 212

Author(s):

Yajun Chen ◽

Zhangnan Wu ◽

Bo Zhao ◽

Caixia Fan ◽

Shuwei Shi

Keyword(s):

Support Vector Machine ◽

Image Segmentation ◽

Feature Fusion ◽

Connected Region ◽

Support Vector ◽

Gray Level ◽

Rotation Invariant ◽

Recognition Model ◽

Corn Seedling ◽

Occurrence Matrix

Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.

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Detection of image region-duplication forgery based on gray level co-occurrence matrix

Journal of Computer Applications ◽

10.3724/sp.j.1087.2011.01628 ◽

2012 ◽

Vol 31 (6) ◽

pp. 1628-1630

Author(s):

Jia-jia OU ◽

Bi-ye CAI ◽

Bing XIONG ◽

Feng LI

Keyword(s):

Gray Level ◽

Image Region ◽

Region Duplication ◽

Occurrence Matrix

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Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

Recent Patents on Engineering ◽

10.2174/1872212112666180601085544 ◽

2019 ◽

Vol 13 (2) ◽

pp. 136-141 ◽

Cited By ~ 2

Author(s):

Abhisek Sethy ◽

Prashanta Kumar Patra ◽

Deepak Ranjan Nayak

Keyword(s):

Feature Extraction ◽

Random Forest ◽

Character Recognition ◽

Recognition Rate ◽

Discrete Wavelet ◽

Gray Level ◽

Handwritten Character Recognition ◽

Handwritten Character ◽

Wide Range ◽

Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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