Adaptive Hypergraph Learning and its Application in Image Classification

By Jun Yu, Dacheng Tao, Meng Wang. IEEE Transactions on Image Processing, 2012

415 citations recorded by the source corpus.

How researchers see this paper

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

Visual Classification by ℓ1-Hypergraph Modeling (2015, methodology): “Finally, our conclusions are presented in Section 5.”
Multimodal Graph-Based Reranking for Web Image Search (2012, background): “One approach is image annotation, which aims to associate several keywords to an image to describe its content based on machine learning and computer vision techniques [2]–[4].”
An Attribute-Assisted Reranking Model for Web Image Search (2015, background): “This strategy is popular to avoid overfitting [13].”
Laplacian Auto-Encoders: An explicit learning of nonlinear data manifold (2015, methodology): “In [32], an adaptive hypergraph learning method is proposed for transductive image classification, where adaptation is realized by simultaneously optimizing the weights of hyperedges and learning the labels of unlabeled images.”
Multi-view hypergraph learning by patch alignment framework (2013, methodology): “It uses KNN to discover relationships among data.”
Local structure preserving discriminative projections for RGB-D sensor-based scene classification (2015, background): “However, differences in viewing angles and lighting variations limit successful scene classification [36,54,58].”