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

• The paper presents an image classification technique which extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains.
• 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 SVM serves as a multiclassifier for image texture features.
• Meanwhile, the particle swarm optimization (PSO) algorithm 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 method can achieve satisfying results and outperform other existing methods.

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.

The following structure displays the conceptual design of the SRITCSD method for image texture classification. More specifically, it depicts the structure of the training phase and the testing phase for the SRITCSD method. In the training phase, the DWT-FE component denotes the feature-extraction scheme applied for a DWT version image. The feature set, fDWTj, in Eq. (8) is computed via feeding the DWT-FE component with a DWT version image. Let ISVDj represent that image jIIj is enhanced via the SVD. Another feature set, fSVD,DWTj, in Eq. (11) is calculated via feeding the DWT-FE component with a DWT version of ISVDj. Also, the SVM performs as a multiclassifier with respect to a set of training patterns which are constructed using image texture features, fDWTj and fSVD,DWTj. Meanwhile, the PSO algorithm is employed to optimize the SRITCSD method, which selects the nearly optimal combination of features and a set of parameters utilized in the SVM. In the testing phase of the SRITCSD method, two feature sets, fDWTq and fSVD,DWTq, are computed for a query image qIIq. The classification result can be obtained via feeding the trained SVM model with fSVD,DWTq to estimate which category the image qIIq belongs to.Figure optionsDownload as PowerPoint slide