Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition

By Zhuolin Jiang, Zhe L. Lin, Larry S. Davis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

1,241 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/96e626c8d03ff83b10adb45901e2e7fe247f2cd4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Lei Zhang
2. David Dapeng Zhang
3. Wangmeng Zuo
4. Xiangchu Feng
5. Meng Yang
6. Jun Xu
7. Shuhang Gu
8. Jingjing Zheng

In Computer Vision and Pattern Recognition

1. Lei Zhang
2. Wangmeng Zuo
3. Luc Van Gool
4. David Dapeng Zhang
5. Ming-Hsuan Yang
6. Radu Timofte
7. Xiangchu Feng
8. Chen Change Loy

Citations in the corpus

• A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, methodology): “In Li et al. (2015) suggested stacking based on simpli-A fied neural network module (SNNM) using four face image datasets (Jiang et al., 2013).”
• Active Lifelong Learning With "Watchdog" (2018, methodology): “The state-of-the-art methods used in this paper include: traditional dictionary learning (TraDL) (Mairal et al. 2009), K-SVD (Aharon, Elad, and Bruckstein 2006), LC-KSVD1 and LC-KSVD2 (Jiang, Lin, and Davis 2013), D-KSVD (Zhang and Li 2010) and ORDL (Lu, Shi, and Jia 2013).”
• Cross-View Action Recognition via Transferable Dictionary Learning (2016, background): “We further consider unsupervised and supervised settings for learning dictionaries.”
• Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, methodology): “We also report the running time of shared dictionary learning method DKSVD (Zhang and Li 2010), class-specific dictionary learning method DLSI (Ramirez et al. 2010), and hybrid dictionary learning method COPAR (Kong and Wang 2012).”
• Semi-Supervised Learning With Graph Learning-Convolutional Networks (2019, methodology): “Scene15.”
• Projective dictionary pair learning for pattern classification (2014, methodology): “Most of the state-of-the-art discriminative DL methods [5, 7, 9] can be formulated under the following framework: minD,A ‖ X − DA ‖(2)F +λ ‖ A ‖p +Ψ(D,A,Y), (1) where λ ≥ 0 is a scalar constant, Y represents the class label matrix of samples in X, D is the synthesis dictionary to be learned, and A i”
• Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising (2015, methodology): “The seminal work of K-SVD [8, 22] has demonstrated promising denoising performance by dictionary learning, which has yet been extended and successfully used in various image processing and computer vision applications [23, 24, 25].”