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Staistical features extraction in wavelet domain for texture classification

Soraya Zehani Abdeldjalil Ouahabi Malika Mimi Abdelmalik Taleb-Ahmed 1, 2 
1 COMNUM - IEMN - COMmunications NUMériques - IEMN
IEMN-DOAE - Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520
Abstract : This paper presents a new approach for texture classification generalizing a well-known statistical features combining the fractal analysis by means of fractal dimension (FD) with the selection first and second order statistics features in the spatial and wavelet domain. The objective of our paper is to propose the features extraction using statistical parameters in the spatial domain and in wavelet domain with different wavelets, with and without preprocessing stage for the texture classification using neural networks for pattern recognition and studying the effect of the preprocessing and wavelets in classification accuracy. The extracted features are used as the input of the ANN classifier. The performance of the proposed methods are evaluated by using two classes of Brodatz database textures. Finally, classification assessment measures such as the confusion matrix, ROC curves and accuracy are applied to the proposed methods.
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Submitted on : Monday, February 14, 2022 - 11:58:09 AM
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Soraya Zehani, Abdeldjalil Ouahabi, Malika Mimi, Abdelmalik Taleb-Ahmed. Staistical features extraction in wavelet domain for texture classification. 6th International Conference on Image and Signal Processing and their Applications (ISPA 2019), Nov 2019, Mostaganem, Algeria. pp.1-5, ⟨10.1109/ISPA48434.2019.8966865⟩. ⟨hal-03572493⟩



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