Kernel Sparse Representation-Based Classifier - IEEE.

SPARSE REPRESENTATION THEORY AND ITS APPLICATION FOR FACE RECOGNITION Yongjiao Wang 1, 2,. Sparse representation based classification for face images has been one of efficient approaches for face recognition in recent years. Discrimination performance by using the sparse representation can also be applied to the face recognition, and any test sample can be expressed as a linear span of the.

Such a sparse representation based classification (SRC) scheme achieves a great success in FR, and it boosts the research of sparsity based pattern classification. Gao et al. (9) proposed the kernel sparse representation for FR, while Yang and Zhang (10) used the Gabor features for SRC with a learned Gabor occlusion dictionary to reduce the computational cost. Cheng et al. (11) discussed the.

Study on Sparse Representation based Classification for.

Most recently, the new emerging sparse representation based classification (SRC) techniques have enjoyed great success in biometric identification area. In the literature (1-4), SRC and its extensions have been ascertained to get significant improvement over conventional classifiers like nearest neighbor (NN), nearest feature line (NFL) and nearest subspace (NS) (5-6) in both recognition.Abstract: In this paper, we address the computational complexity issue in Sparse Representation based Classification (SRC). In SRC, it is time consuming to find a global sparse representation. To remedy this deficiency, we propose a Local Sparse Representation based Classification (LSRC) scheme, which performs sparse decomposition in local neighborhood.In classical sparse representation based classification (SRC) and weighted SRC (WSRC) algorithms, the test samples are sparely represented by all training samples. They emphasize the sparsity of the coding coefficients but without considering the local structure of the input data. Although the more training samples, the better the sparse representation, it is time consuming to find a global.


Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the.Critical parameters of the sparse representation-based classifier Abstract: In recent years, the growing attention in the study of the compressive sensing (CS) theory suggested a novel classification algorithm called sparse representation-based classifier (SRC), which obtained promising results by casting classification as a sparse representation problem. Whereas SRC has been applied to.

In this chapter, we show how the sparse representation framework can be used to develop robust algorithms for object classification (156), (112), (106). In particular, we will outline the Sparse Representation-based Classification (SRC) algorithm (156) and present its applications in robust biometrics recognition (156), (112), (111).

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Sparse representation with learning-based overcomplete dictionaries has recently achieved impressive results in signal representation and outperforms traditional methods in tasks such as signal denosing and image inpainting. Inference algorithms also benefit from sparse representation because of its effectiveness in removing data corruption such as dense noise and partial occlusion and the.

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What is Sparse Representation. 1. SP deals with sparse solutions for systems of linear equations. It is a greedy principle that signal can be approximated by a sparse superposition of basis functions. Learn more in: A Novel Approach of K-SVD-Based Algorithm for Image Denoising Find more terms and definitions using our Dictionary Search. Sparse Representation appears in: Histopathological Image.

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An image classification method based on sparse representation with basis design is proposed. We construct a classification model under the sparse representation theory. The sparse model can lead to be better performance under a suitable dictionary, so the basis design method can follow the same process as discussed in(6).The experiments show that the proposed method improves the result.

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This website introduces a new mathematical framework for classification and recognition problems in computer vision, especially face recognition. The basic idea is to cast recognition as a sparse representation problem, utilizing new mathematical tools from compressed sensing and L1 minimization. This leads to highly robust, scalable algorithms for face recognition based on linear or convex.

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Recently, sparse representation based classification (SRC) method has been shown to provide satisfactory classification accuracy in motor imagery classification. In this paper, we aim to evaluate.

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Based on this observation, a sparse-representation-based classification (SRC) has been proposed for robust face recognition and has gained popularity for various classification tasks. It relies on the underlying assumption that a test sample can be linearly represented by a small number of training samples from the same class. However, SRC implementations ignore the Euclidean distance.

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Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel supervised structure dictionary learning (SSDL) algorithm to learn a discriminative and block structure dictionary. We associate label information with each dictionary item and make each class-specific sub.

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Sparse representation has attracted great attention in the past few years. Sparse representation based classification (SRC) algorithm was developed and successfully used for classification. In this paper, a kernel sparse representation based classification (KSRC) algorithm is proposed. Samples are mapped into a high dimensional feature space first and then SRC is performed in this new feature.

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Among all image representation and classification methods, sparse representation has proven to be an extremely powerful tool. However, a limited number of training samples are an unavoidable problem for sparse representation methods. Many efforts have been devoted to improve the performance of sparse representation methods. In this study, the authors proposed a novel framework to improve the.

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