Model Based Texture Features

Chaki, Jyotismita; Dey, Nilanjan

doi:10.1007/978-981-15-0853-0_5

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…The coefficients computed by these models then characterize the image texture. Random mosaic model, Autoregressive Model, Markov-random Fields model, Gibbs-random Fields, and Fractal Dimension are examples of Model-based methods [25].…”

Section: B Textural Analysis Methodsmentioning

confidence: 99%

Textural Analysis for Medicinal Plants Identification Using Log Gabor Filters

et al. 2022

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Texture plays a crucial role in computer vision, providing valuable information about image regions. Log Gabor filters that mimic the human eye's visual cortex are used as feature extractors to identify medicinal plants based on the leaf textural features. This method was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana, consisting of forty-nine (49) plant species as well as the Flavia and Swedish Leaf datasets, which are benchmark datasets. The Log Gabor filter outperformed the Gabor filters, which have been extensively used in this area when tested on nine supervised classifiers (K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Logistic Regression, Decision tree, Random Forest, Multilayer Perceptron, Gradient Boosting and Stochastic Gradient Descent) with 10-fold cross-validation. The Support Vector Machine and Multilayer Perceptron were the best performing classifiers for both Log Gabor filter and Gabor filter in terms of accuracy, precision, true positive rate, F1 score and false positive rate. The Log Gabor filter's highest accuracy was 79% for Mydatastet, 97% for Flavia, and 98% for the Swedish Leaf dataset whiles the Gabor filter's highest accuracy was 66% for Mydatastet, 92% for Flavia and 96% for the Swedish Leaf dataset.

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Section: B Textural Analysis Methodsmentioning

confidence: 99%