Kernel Sparse Representation for Image Classification and Face Recognition

Gao, Shenghua; Tsang, Ivor W.; Chia, Liang-Tien

doi:10.1007/978-3-642-15561-1_1

Cited by 258 publications

(200 citation statements)

References 16 publications

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“…The comparison algorithms include KMTJSRC [37] only considering the block sparsity in RKHS, KSR [10] only considering the element sparsity in RKHS, the representatives of multiple kernel learning (MKL) methods, e.g., [25].…”

Section: Optimizationmentioning

confidence: 99%

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

et al. 2015

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This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

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Section: Optimizationmentioning

confidence: 99%

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

et al. 2015

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“…Gao et al [28] proposed the kernel sparse representation for FR and image classification, while FR with continuous occlusion and misalignment via sparse representation have been presented in [29][33] [34].…”

Section: Introductionmentioning

confidence: 99%

“…Such a big occlusion matrix makes the sparse coding process very computationally expensive, and even prohibitive. These two issues are not fully solved by the sparsity based FR improvers [28][29][30][31][32][33][34] [40][41][42]. For instance, only holistic features are considered in [29][30][31][32][33][34][ [40][41][42], FR with occlusion is ignored in [28][32] [33], and no occlusion dictionary is learned in [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

Yang

Zhang

Shiu

et al. 2013

Pattern Recognition

140

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By representing the input testing image as a sparse linear combination of the training samples via l 1 -norm minimization, sparse representation based classification (SRC) has shown promising results for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to code the occluded portions of face images, SRC could lead to robust FR results against face occlusion. However, the l 1 -norm minimization and the high number of atoms in the identity occlusion dictionary make the SRC scheme computationally very expensive. In this paper, a Gabor feature based robust representation and classification (GRRC) scheme is proposed for robust FR. The use of Gabor features not only increases the discrimination power of face representation, but also allows us to compute a compact Gabor occlusion dictionary which has much less atoms than the identity occlusion dictionary. Furthermore, we show that with Gabor feature transformation, l 2 -norm could take place the role of l 1 -norm to regularize the coding coefficients, which reduces significantly the computational cost in coding occluded face images. Our extensive experiments on benchmark face databases, which have variations of lighting, expression, pose and occlusion, demonstrated the high effectiveness and efficiency of the proposed GRRC method.

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“…For the sparse representation based scheme, [23] proposed kernel K-SVD and kernel MOD methods. [4] proposed kernel sparse representation (KSR), where the dictionary is trimmed to work well with a simplified Gaussian Mixture Model which can be viewed as a solution to density estimation problems. It generally outperforms the previous alternative extensions of sparse representation for image classification and face recognition.…”

Section: Introductionmentioning

confidence: 99%

Self-explanatory Sparse Representation for Image Classification

Liu

Wang

Shen

et al. 2014

Computer Vision – ECCV 2014

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Abstract. Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory reformulation of sparse representation, i.e., linking the learned dictionary atoms with the original feature spaces explicitly, to extend simultaneous dictionary learning and sparse coding into reproducing kernel Hilbert spaces (RKHS). The resulting single-view self-explanatory sparse representation (SSSR) is applicable to an arbitrary kernel space and has the nice property that the derivatives with respect to parameters of the coding are independent of the chosen kernel. With SSSR, multiple-view self-explanatory sparse representation (MSSR) is proposed to capture and combine various salient regions and structures from different kernel spaces. This is equivalent to learning a nonlinear structured dictionary, whose complexity is reduced by learning a set of smaller dictionary blocks via SSSR. SSSR and MSSR are then incorporated into a spatial pyramid matching framework and developed for image classification. Extensive experimental results on four benchmark datasets, including UIUC-Sports, Scene 15, Caltech-101, and Caltech-256, demonstrate the effectiveness of our proposed algorithm.

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Kernel Sparse Representation for Image Classification and Face Recognition

Cited by 258 publications

References 16 publications

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

Self-explanatory Sparse Representation for Image Classification

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