Learning a discriminative dictionary for sparse coding via label consistent K-SVD

By Zhuolin Jiang, Zhe L. Lin, Larry S. Davis. Computer Vision and Pattern Recognition, 2011

784 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/50ee027c63dcc5ab5cd0a6cdffb1994f83916a46

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Sparse coding based visual tracking: Review and experimental comparison (2013, methodology): “For dictionary learning, there are some effective discriminative dictionary methods [65,66] proposed in object recognition, which could be modiﬁed to be used in visual tracking.”
Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification (2015, methodology): “There are three types of features: 1) Hand-crafted features, which include SIFT[1], HoG2x2[2, 55, 58], LBP[14], GIST[15], and CENTRIST[56]; 2) The baseline features learned in unsupervised way without encoding discriminative or shareable information, which include feature extracted with Random weigh”
Multipath Sparse Coding Using Hierarchical Matching Pursuit (2013, background): “LLC [28], LCKSVD [13], and Asklocals [5] are one-layer sparse coding approaches.”
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding (2016, background): “…is the sparse representation concept, being one of the most popular choices for a prior in the signal and image processing communities, and leading to exceptional results in various applications (Elad and Aharon, 2006; Dong et al., 2011; Zhang and Li, 2010; Jiang et al., 2011; Mairal et al., 2014).”
Cross-View Action Recognition via Transferable Dictionary Learning (2016, background): “We further consider unsupervised and supervised settings for learning dictionaries.”
Removing Rain from a Single Image via Discriminative Sparse Coding (2015, methodology): “[13, 18, 27]) for sparse coding based image classiﬁcation, we propose a discriminative sparse coding method to regularize the image de-raining process under over learned dictionary with mutual exclusivity property.”
Top-down visual saliency via joint CRF and dictionary learning (2012, methodology): “Note that our model is different from the hidden CRFs [25], where Quattoni et al. define a CRF of latent variables to represent the part features of local patches and a single output variable to describe the image category.”
Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016, methodology): “Fanello et al. [35], [36] extracted motion and gradient features (3D Histograms of Scene Flow and Global HOGs), and then adopted sparse coding [44], [45] to capture high-level patterns from the extracted features, and ﬁnally used a sliding window with linear support vector machines (SVMs) [46],…”
Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps (2013, background): “[6] further increased the discrimination by adding a label consistent term.”
Learning View-Invariant Sparse Representations for Cross-View Action Recognition (2013, methodology): “We incorporate the discriminative sparse code error term introduced in [9] to achieve this goal.”