An efficient texture classification algorithm using integrated Discrete Wavelet Transform and local binary pattern features

2018 ◽  
Vol 52 ◽  
pp. 267-274 ◽  
Author(s):  
K. Gopala Krishnan ◽  
P.T. Vanathi
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Local Binary Pattern ◽  
Texture Classification ◽  
Classification Algorithm ◽  
Discrete Wavelet

scholarly journals Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

2019 ◽  
Vol 9 (6) ◽  
pp. 5253
Author(s):  
Fthi M. Albkosh ◽  
Muhammad Suzuri Hitam ◽  
Wan Nural Jawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Rozniza Ali
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Texture Classification ◽  
Optimization Technique ◽  
Image Texture ◽  
Discrete Wavelet ◽  
Bee Colony ◽  
Selection Of

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.

scholarly journals EKTRAKSI FITUR MENGGUNAKAN DISCRETE WAVELET TRANSFORM DAN FULL NEIGHBOUR LOCAL BINARY PATTERN UNTUK KLASIFIKASI MAMMOGRAM

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Januar Adi Putra ◽  
Nanik Suciati ◽  
Arya Yudhi Wijaya
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Local Binary Pattern ◽  
New Method ◽  
Discrete Wavelet ◽  
Backpropagation Neural Network ◽  
Average Accuracy ◽  
Mammogram Image ◽  
Better Than

[Id]Local binary pattern adalah sebuah kode biner yang menggambarkan pola tekstur lokal. Hal ini dibangun dengan lingkungan batas dengan nilai abu-abu dari pusatnya. Local binary pattern tradisional memiliki beberapa kelemahan yakni varian terhadap rotasi dan pada saat proses thresholding pixel sensitif terhadap noise. Pada penelitian ini diusulkan sebuah metode ektraksi fitur baru untuk mengatasi masalah tersebut, metode tersebut disebut full neighbour local binary pattern (fnlbp). Metode ini nantinya akan dikombinasikan dengan discrete wavelet transform untuk ektraksi fitur dari citra mammogram dengan metode klasifikasi adalah Backpropagation Neural Network (BPNN). Berdasar ujicoba yang telah dilakukan metode usulan mendapatkan rata-rata akurasi yang lebih baik daripada metode local binary pattern tradisional baik yang dikombinasi dengan discrete wavelet transform ataupun tidak. Performa metode usulan full neighbour local binary pattern dapat menghasilkan akurasi yang sempurna yakni 100% baik pada saat menggunakan discrete wavelet transform ataupun tidak, sedangkan akurasi terendah yang didapat adalah 90.49%.Kata Kunci: Ekstraksi fitur, local binary pattern, wavelet, klasifikasi mammogram.[En]Traditional local binary pattern have some disadvantages which is a variant of the rotation and during the thresholding process the pixel is sensitive to noise. At this study the authors proposed a new method of features extraction to solve that problem and this method called full neighbor local binary pattern (fnlbp). This method will be combined with discrete wavelet transform to extract the features of the mammogram image and the classification method is Backpro- pagation Neural Network (BPNN). Based on experiments the result of proposed method in an average accuracy is better than traditional methods of local binary pattern which combined with discrete wavelet transform or not. The performance of the proposed method of full neighbor local binary pattern can produce perfect accuracy that is 100%, this accuracy is reached when using discrete wavelet transform or not, while the lowest accuracy obtained is 90.49%.

Sign in / Sign up

Export Citation Format

Share Document