Texture Characterization by Grey-Level Co-occurrence Matrix from a Perceptual Approach

Gebejes, Ana; Huertas, Rafael; Trémeau, Alain; Tomić, Ivana; Biswas, PooshpanjanR.; Fraza, Charlotte; Hauta-Kasari, Markku

doi:10.2352/issn.2169-2629.2017.32.271

Cited by 53 publications

(73 citation statements)

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“…Figure 2 shows the impact of earthquake damage on two selected textural and structural features. Entropy, energy, dissimilarity, and homogeneity are all second order texture features derived from the GLCM that have been correlated with damage or used as proxies for damage in previous studies [35,[37][38][39]. In order to reduce dimensionality and eliminate redundancy, the two consistently correlated GLCM features of Entropy (a measure of gray level randomness) and Dissimilarity (a measure of gray level difference (the square of contrast)) were chosen as texture inputs.…”

Section: Texture and Structurementioning

confidence: 99%

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

2016

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Abstract:Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (M w ) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.

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Section: Texture and Structurementioning

confidence: 99%

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

2016

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“…Mathematical formulation of the mean is given in (2) Homogeneity measures the uniformity of the non-zero entries in the gray level Co-occurrence Matrix (GLCM). It weights values by the inverse of contrast weight [3] The GLCM homogeneity of any texture is high if GLCM concentrates along the diagonal, meaning that there are a lot of pixels with the same or very similar grey level value. The larger the changes in grey values, the lower the GLCM homogeneity making higher the GLCM contrast.…”

Section: Fig 1 Flow Chart For Building the Semantic Indexed Databasementioning

confidence: 99%

An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques

Alebiary¹,

Semary²,

Zayed³

2017

IJCA

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Content-based Image Retrieval (CBIR) is retrieving the desired images from huge collections. The user queries are becoming very specific and traditional text-based methods cannot efficiently handle them. CBIR system retrieves the image via low-level features such as color, texture and shape. In this work, we propose CBIR system that retrieves images from a database based on the semantic features of them.Our methodology divide the query image into 100 regions. And then, extracts Features Vector from each region and label each one with the suitable concept like (Sky, Sand, Water, trunks, foliage, rocks,..., and Grass). The labeling process in performed semi-automatically using k-means clustering and KNN classification algorithms. The system has been evaluated by recall and precision measures and compared to other recent works. The results of the paper reflects the efficiency of the system for retrieving images with up to 98% recognition ratio. General TermsImage processing, Image representations, Visual contentbased indexing and retrieval.

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“…Features were extracted from the Co-occurrence matrix by calculating contrast, energy, homogeneity, correlation, and dissimilarity. To calculate the features from the matrix, the following equations were calculated (18,19) :…”

Section: Feature Extractionmentioning

confidence: 99%

Classifying Human and Animal Hair Using Probabilistic Neural Networks for Texture Classification

Donato¹,

Famador²

2017

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

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Hair Analysis has been widely used in the field of forensics especially in finding out if a piece of hair evidence in a crime scene is considered valid. This study proposes a classification method for human head hair and animal hair using Probabilistic Neural Network. Hair samples were mounted in a compound microscope, digitized using a camera and stitched using method used by Rosebrock. Images were magnified 100 times and 400 times. Sobel edge detection was used for texture analysis. Gray-level cooccurrence matrix was applied to all samples to extract features. Results were then fed to a Probabilistic Neural Network for classification. The datasets were validated using the 2-fold cross validation, wherein 2 training sets and 2 test sets were made. An 84.615 percent accuracy was achieved for normalized data and a 100 percent accuracy was achieved for unnormalized data.

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Texture Characterization by Grey-Level Co-occurrence Matrix from a Perceptual Approach

Cited by 53 publications

References 0 publications

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques

Classifying Human and Animal Hair Using Probabilistic Neural Networks for Texture Classification

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