Абстрактный

Texture Classification with Local Binary Pattern Based on Continues Wavelet Transformation

H. R. Eghtesad Doost, M. C. Amirani

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.