SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains

Abstract

The paper presents a new image classification technique which first extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains. Subsequently, it exploits a support vector machine (SVM) to perform image texture classification. For convenience, it is called the SRITCSD method hereafter. First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image. Also, the SRITCSD method employs the SVM to serve as a multiclassifier for image texture features. Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM. The experimental results demonstrate that the SRITCSD method can achieve satisfying results and outperform other existing methods under considerations here.