Learn More
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)
This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. Abstract(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)
Compressed sensing (CS) theory considers the restricted isometry property (RIP) as a sufficient condition for measurement matrix which guarantees the recovery of any sparse signal from its compressed measurements. The RIP condition also preserves enough information for classification of sparse symbols, even with fewer measurements. In this work, we utilize(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)
  • 1