Locality-constrained Linear Coding for image classification

By Jinjun Wang, Jianchao Yang, Kai Yu, Fengjun Lv, Thomas S. Huang, Yihong Gong. 2010

3,382 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Going deeper with convolutions (2014, methodology): “Recent work has focused on motion descriptors that are invariant to camera motion [5, 6, 8, 10, 11, 12, 13, 14].”
ImageNet Large Scale Visual Recognition Challenge (2014, methodology): “2006) features with two non-linear coding representations (Zhou et al. 2010; Wang et al. 2010) and a stochastic SVM.”
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (2014, methodology): “, [30,28,21]) and detection (e.”
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (2013, methodology): “In particular, we compare the features described in Section 3 with GIST features (Oliva & Torralba, 2001) and LLC features (Wang et al., 2010).”
Understanding deep image representations by inverting them (2014, background): “Most image understanding and computer vision methods build on image representations such as textons [17], histogram of oriented gradients (SIFT [20] and HOG [4]), bag of visual words [3][27], sparse [37] and local coding [34], super vector coding [40], VLAD [10], Fisher Vectors [23], and, lately, de”
Genetic CNN (2017, neutral): “, GPU), the Convolutional Neural Networks (CNNs) [19][32] have shown superior performance over the conventional Bag-ofVisual-Words [3][38][29] and compositional models [9].”
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, methodology): “For RGB signals, the features are HOG, HOF, MBHX, and MBHY descriptors of dense trajectories [71], followed by a K-means clustering and locality-constrained linear coding (LLC) [75] to calculate their global representations as bags-of-features.”
Multimodal Multipart Learning for Action Recognition in Depth Videos (2015, methodology): “We encode HON4D features using LLC (localityconstrained linear coding) [44] to reduce their dimensionality while preserving the locality of 4D surface normals.”
Efficient and accurate approximations of nonlinear convolutional networks (2014, background): “In theory, λ should be gradually increased to infinity [23].”
Understanding Image Representations by Measuring Their Equivariance and Equivalence (2014, background): “Notable examples include textons [11], histogram of oriented gradients (SIFT [14] and HOG [4]), bag of visual words [3][24], sparse [32] and local coding [31], super vector coding [35], VLAD [9], Fisher Vectors [17], and the latest generation of deep convolutional networks [10, 21, 33].”
Visualizing Deep Convolutional Neural Networks Using Natural Pre-images (2015, background): “2010) and local coding (Wang et al. 2010), super vector coding (Zhou et al.”
Undoing the Damage of Dataset Bias (2012, methodology): “Then, we use Locality-constrained Linear Coding (LLC) [22] to assign the descriptors to codewords.”
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice (2014, positive): “Reconstruction based encoding methods [61,50,63,42] are designed from the perspective of decoding process, where the codes s are enforced to reconstruct the input descriptor x.”
HCP: A Flexible CNN Framework for Multi-Label Image Classification (2014, methodology): “, SIFT [29], along with the bag-of-words coding scheme, followed by the feature pooling [24], [32], [39] and classic classifiers, such as Support Vector Machine (SVM) [5] and random forests [3].”
Reading Text in the Wild with Convolutional Neural Networks (2014, background): “– 647 SVT-50 SVT [55] test dataset with fixed lexicon.”