A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given textur...

Sparse representations account for most or all of the information of a signal by a linear combination of a few elementary signals called atoms, and have increasingly become recognized as providing high performance for applications as diverse as noise reduction, compression, inpainting, compressive sensing, pattern classification, and blind source separation. In this dissertation, we learn the s...

Sparse representation has become very popular in fields of signal processing, image processing computer vision and pattern recognition. Sparse representation also has good reputation in both theoretical and practical applications. Images can be sparsely coded by structural primitives and recently the sparse coding or sparse representation has been widely used to resolve the problems in image re...

Epilepsy seizure detection in Electroencephalogram (EEG) is a major issue in the diagnosis of epilepsy and it can be considered as a classification problem. According to the particular property of EEG, a novel method based on sparse representation is proposed for epilepsy detection in this paper. Classification accuracy, robustness on noisy data and parameters (the size of dictionary and the nu...

We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations th...

We propose a novel approach to acoustic modeling based on recent advances in sparse representations. The key idea in sparse coding is to compute a compressed local representation of a signal via an over-complete basis or dictionary that is learned in an unsupervised way. In this study, we compute the local representation on speech spectrogram as the raw “signal” and use it as the local sparse c...

Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent ye...

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such ...