An Efficient BRINT-Random Forests Algorithm Based Texture Classification

Abstract: Texture analysis is considered fundamental and important in the field of pattern to computationally represent an intuitive perception of texture and to facilitate automatic processing of the texture information for artificial vision systems. The texture classification methods based on local binary patterns (LBP),Scale Invariant Feature Transform(SIFT),Binary Rotation Invariant And Noise Tolerant Texture Classification (BRINT), Nearest Neighborhood Classifier(NNC) etc. performs texture classification with accuracy the need of high training samples and increased time consumption are the major challenges. In this paper, random forest algorithm is used to deal with the problem of texture classification. The proposed classifier consists of a number of trees, with each tree grown using some form of randomization. The leaf nodes of each tree are labeled by estimates of the posterior distribution over the image classes. Each internal node contains a test that best splits the space of data to be classified. Time consumption can be reduced considerably because of this random forest Algorithm. The proposed algorithm is having high accuracy with less time consumption. Keywords: LPB, SIFT, BRINT-NNC Classifier.