Fisher Discrimination Dictionary Learning for sparse representation

By Meng Yang, Lei Zhang, Xiangchu Feng, David Dapeng Zhang. Vision, 2011

1,004 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/544d6cd24db5adad8453033e0cc1aa7d3d6224ab

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps (2013, background): “[23] proposed to add the Fisher discrimination criterion into the dictionary learning.”
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, background): “…sparse representation techniques have also led to promising results in face recognition (Wright et al. 2009b; Wang et al. 2009; Yang and Zhang 2010; Yang et al. 2011a,b), handwritten digit and texture classification (Huang and Aviyente 2006; Mairal et al. 2009; Ramirez et al. 2010; Yang et al.…”
Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, methodology): “(8) can be improved as (Yang et al., 2011): min D;a fDREðD;X;aÞ þ k1CSðaÞ þ k2CDðaÞg ð11Þ where DRE(D, X, a) is discriminative reconstruction error, CS(a) is coefficient sparsity, CD(a) is coefficient discrimination, and k1 and k2 are constants.”
Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification (2015, methodology): “In the feature engineering area, hand-crafted features including SIFT [1], HOG [2], LBP [14] and GIST [15] (global feature) were popular used.”
Sparse Modeling for Image and Vision Processing (2014, background): “Then, joint cost functions that involve both a discriminative term and a classical dictionary learning formulation have been proposed several times in the literature [Rodriguez and Sapiro, 2008, Mairal et al., 2008b, Pham and Venkatesh, 2008, Yang et al., 2011].”
Structure-Preserved Multi-source Domain Adaptation (2016, background): “the adaptation process to shift the discriminative function from the source domain and applies it for the target domain [35]; Low-rank Transfer Subspace Learning (LTSL) first learns a subspace with the low-rank constraints, then applies PCA or LDA for the adaptation [29]; SGF trains the discriminati”
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”
Generalized Domain-Adaptive Dictionaries (2013, methodology): “learning algorithm as FDDL. Update˜B as: ˜ B = Z † D 3.”
Learning Sparse FRAME Models for Natural Image Patterns (2014, background): “resentation for the task of domain transfer on four domain datasets, and compare with published results (Saenko et al. 2010; Gopalan et al. 2011; Gong et al. 2012; Yang et al. 2011; Jhou et al. 2012; Hoffman et al. 2013; Shekhar et al. 2013).”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “quired in different conditions [17, 18, 19, 20, 21], video concept detection [22], video event recognition [23], activity recognition [24, 25], human motion parsing from videos [26], face recognition [27, 28, 29], facial landmark localization [30], facial action unit detection [31], 3D pose estimati”
Learning Discriminant Face Descriptor (2014, methodology): “In object recognition, a number of encoding methods like sparse coding [34] and discriminant dictionary learning [35] are proposed to extract robust features.”
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2016, background): “Consequently, our work is also partly related with the ideas of learning invariant features such as [40-46] and the approaches incorporating discriminative terms into model training such as [47, 48].”