Texture Classification with Local Binary Pattern Based on Continues Wavelet Transformation

Abstract: In this paper, a new algorithm which is based on the continues wavelet transformation and local binary patterns (LBP) for content based texture image classification is proposed. We improve the Local Binary Pattern approach with Wavelet Transformation to propose the texture classification. We used 12 classes of Brodatz textures data base for proposed method. Each class is divided to 64 texture image and then wavelet transformation is applied to each texture. After transformed texture from wavelet the feature extraction matrix is formation using LBP. The same concept is utilized at LBP calculation which is generating nine LBP patterns from a given 3×3 pattern. Finally, nine LBP histograms are calculated which are used as a feature vector for image classification. Two experiments have been carried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiments are Brodatz database. We verify the other method and proposed method is very good and efficient for classification texture image

A new feature extraction approach based on non linear source separation

A new feature extraction approach is proposed in this paper to improve the classification performance in remotely sensed data. The proposed method is based on a primary sources subset (PSS) obtained by nonlinear transform that provides lower space for land pattern recognition. First, the underlying sources are approximated using multilayer neural networks. Given that, Bayesian inferences update unknown sources’ knowledge and model parameters with information’s data. Then, a source dimension minimizing technique is adopted to provide more efficient land cover description. The support vector machine (SVM) scheme is developed by using feature extraction. The experimental results on real multispectral imagery demonstrates that the proposed approach ensures efficient feature extraction by using several descriptors for texture identification and multiscale analysis. In a pixel based approach, the reduced PSS space improved the overall classification accuracy by 13% and reaches 82%. Using texture and multi resolution descriptors, the overall accuracy is 75.87% for the original observations, while using the reduced source space the overall accuracy reaches 81.67% when using jointly wavelet and Gabor transform and 86.67% when using Gabor transform. Thus, the source space enhanced the feature extraction process and allow more land use discrimination than the multispectral observations