Graph Regularized Sparse Coding for Image Representation

By Miao Zheng, Jiajun Bu, Chun Chen, C. Wang, Lijun Zhang, Guang Qiu, Deng Cai. IEEE Transactions on Image Processing, 2011

576 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4eb130a05b2fc9cad0bd97fd85500a71ad5cbd66

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Generating Diverse Image Datasets with Limited Labeling (2016, background): “, active learning [25, 17, 2], sparse coding [11, 32, 36] and deep learning [22, 21].”
Transfer Sparse Coding for Robust Image Representation (2013, methodology): “[24] proposed a Graph Regularized Sparse Coding (GraphSC) method, which further explores the manifold assumption [2].”
Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (unknown): “Before we introduce our proposed model, in this section, we review the recent novel methods such as LRR [28] [29] [30] and graph based analysis [2] [45] [50].”
How to Learn a Graph from Smooth Signals (2016, background): “(1) Zhang, Popescul, and Dom (2006) use it to enhance web page categorization with graph information, Zheng et al. (2011) for graph regularized sparse coding.”
Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images (2013, methodology): “Inspired by recent works [33], [35], a graph regularizer is incorporated into the LRR technique to preserve the intrinsic geometrical structure of the data.”
Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection (2017, methodology): “Feature selection methods, such as statistical t-test and sparse linear regression, find informative feature subsets from original feature set [8], [14], [15], while subspace learning methods, such as Fisher’s linear discriminant analysis, canonical correlation analysis [16], [17], and locality pres”
Unsupervised Cross-Dataset Transfer Learning for Person Re-identification (2016, background): “Graph Laplacian regularisation has also been explored in a sparse coding formulation before, for problems such as unsupervised clustering [8, 42], or supervised face verification/recognition [13].”
Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization (2016, methodology): “2 Unified Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization Unlike the existing graph regularized sparse coding approaches [13, 11], we introduce the probability neighborhood matrix [6] that learns the data similarity matrix and sparse coding simultaneously.”