An analysis of co-occurrence texture statistics as a function of grey level quantization

By David A Clausi. 2002

1,087 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/80bc248c4a5a8bdfbfd4382de6264d426608988e

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Object-oriented mapping of landslides using Random Forests (2011, background): “Although the optimal choice of the texture measures depends to a certain degree on the application, it is interesting to note that Clausi (2002) highlighted Con.”
A methodological approach to the classification of dermoscopy images (2007, methodology): “Here, the choice of 64 gray levels was arbitrary, though a recent study [ 26 ] has demonstrated that this value should not be too low (for example below 24).”
Remote Sensing Image Classification: No Features, No Clustering (2015, methodology): “The statistics we compute are autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, entropy, homogeneity, maximum probability, sum of squares, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlati”
Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery (2009, methodology): “Texture values for each of the sample objects for the three classes were calculated and imported into the decision tree analysis software CART by Salford Systems, which implements the algorithm developed by Clausi [28].”
Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods (2020, methodology): “the second phase, six different feature extraction methods as Grey Level Cooccurrence Matrix (GLCM) [18-20], Grey Level Run Length Matrix (GLRLM) [21], Grey”
DeepSat: a learning framework for satellite imagery (2015, methodology): “These features were shown to be useful descriptors for classification of satellite imagery in previous studies ([14], [32], [10]).”
Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms (2013, methodology): “Some researchers [61, 117, 118, 134, 135] used GLCM-based texture features [136– 138] like dissimilarity, contrast, energy, maximum probability, correlation, entropy, and so forth.”
A Galaxy of Texture Features (2008, methodology): “(37) Texture spectrum ( ♥ − −) Similar to the texton approach, the texture spectrum method 84 considers a texture image a composition of texture units and uses the global distribution of these units to characterise textures.”
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. (2016, background): “th lower weights for elements that are further from the diagonal. F cm:inv:di:mom = XN g i=1 XN g j=1 p ij 1 + (i j)2 CHAPTER 4. IMAGE FEATURES 41 4.6.17 Inverse difference moment normalised Clausi (Clausi, 2002) suggests normalising inverse difference moment to improve classiﬁcation performance of ”
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture (2012, background): “(1973) have been found to be highly correlated (Clausi, 2002).”