Sparse approximation

About: Sparse approximation is a research topic. Over the lifetime, 18037 publications have been published within this topic receiving 497739 citations. The topic is also known as: Sparse approximation.

Feature-Based Image Patch Approximation for Lung Tissue Classification

Abstract: In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.

Basic pursuit of an adaptive impulse dictionary for bearing fault diagnosis

Abstract: The sparse decomposition based on basic pursuit is an adaptive sparse expression of the signals. An adaptive basic pursuit algorithm that uses an impulse dictionary is introduced in this article for rolling bearing vibration signal processing and fault diagnosis. In the first, a new dictionary model is constructed according to the characteristics and mechanism of rolling bearing faults. Rotational speed of bearing, dimensions of bearing and bearing fault situation, among other parameters incorporates the new mode which could simulate the real fault impulse in different fault levels. Secondly a new method of dictionary establishment called adaptive impulse dictionary combine with basic pursuit introduced in this paper for bearing fault diagnosis. Adaptive impulse dictionary is created by changing characteristic parameters progressively, which make the dictionary built by this method has a lower redundancy and a higher relevance between each dictionary atom and the analyzed vibration signal. The basic pursuit algorithm of an adaptive impulse dictionary is adopted to analyze experiment signal of the early-stage fault signals and composite fault signals of the bearing. The results demonstrate the effectiveness of the basic pursuit algorithm that uses the adaptive impulse dictionary.

Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person

Abstract: Face recognition (FR) with a single training sample per person (STSPP) is a very challenging problem due to the lack of information to predict the variations in the query sample. Sparse representation based classification has shown interesting results in robust FR, however, its performance will deteriorate much for FR with STSPP. To address this issue, in this paper we learn a sparse variation dictionary from a generic training set to improve the query sample representation by STSPP. Instead of learning from the generic training set independently w.r.t. the gallery set, the proposed sparse variation dictionary learning (SVDL) method is adaptive to the gallery set by jointly learning a projection to connect the generic training set with the gallery set. The learnt sparse variation dictionary can be easily integrated into the framework of sparse representation based classification so that various variations in face images, including illumination, expression, occlusion, pose, etc., can be better handled. Experiments on the large-scale CMU Multi-PIE, FRGC and LFW databases demonstrate the promising performance of SVDL on FR with STSPP.

Polyphonic music transcription by non-negative sparse coding of power spectra

Abstract: We present a system for adaptive spectral basis decomposition that learns to identify independent spectral features given a sequence of short-term Fourier spectra When applied to recordings of polyphonic piano music, the individual notes are identified as salient features, and hence each short-term spectrum is decomposed into a sum of note spectra; the resulting encoding can be used as a basis for polyphonic transcription The system is based on a probabilistic model equivalent to a form of noisy independent component analysis (ICA) or sparse coding with non-negativity constraints We introduce a novel modification to this model that recognises that a short-term Fourier spectrum can be thought of as a noisy realisation of the power spectral density of an underlying Gaussian process, where the noise is essentially multiplicative and non-Gaussian Results are presented for an analysis of a live recording of polyphonic piano music