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Digital information is packed into files when it is going to be stored on storage media. Each computer file is associated with a type. Type detection of computer data is a building block in different applications of computer forensics and security. Traditional methods were based on file extensions and metadata. The content-based method is a newer approach(More)
Automated and accurate classification of brain MRI is such important that leads us to present a new robust classification technique for analyzing magnetic response images. The proposed method consists of three stages, namely, feature extraction, dimensionality reduction, and classification. We use gray level co-occurrence matrix (GLCM) to extract features(More)
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In this paper, we present a new feature extraction method for Electroencephalogram (EEG) signals classification, based on GARCH modeling of wavelet coefficients. First, the EEG signals are decomposed into the frequency sub-bands using discrete wavelet transform (DWT). GARCH model can capture the important statistical properties of wavelet coefficients. We(More)
Utilization of cyclostationarity is a fresh paradigm in texture classification. This paper employs the Strip Spectral Correlation Analyzer (SSCA) as the new and superior method of such a category. The SSCA has been much more computational efficient than the other spectral correlation estimators, such as the FFT-Accumulated Method (FAM) or Direct Frequency(More)
Motivated by the recent success of deep networks in providing effective and abstract image representations, in this paper, a multi-layer architecture called the multi-layer local energy patterns (ML-LEP) is proposed for texture representation and classification. The proposed approach follows a multi-layer convolutional neural network paradigm and is built(More)
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.(More)