Element-Wise Feature Relation Learning Network for Cross-Spectral Image Patch Matching - Apprentissage de modèles visuels à partir de données massives Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Neural Networks and Learning Systems Année : 2022

Element-Wise Feature Relation Learning Network for Cross-Spectral Image Patch Matching

Dou Quan
Shuang Wang
Ning Huyan
Ruojing Wang
  • Fonction : Auteur
Xuefeng Liang
Biao Hou
Licheng Jiao

Résumé

Recently, the majority of successful matching approaches are based on convolutional neural networks, which focus on learning the invariant and discriminative features for individual image patches based on image content. However, the image patch matching task is essentially to predict the matching relationship of patch pairs, that is, matching (similar) or non-matching (dissimilar). Therefore, we consider that the feature relation (FR) learning is more important than individual feature learning for image patch matching problem. Motivated by this, we propose an element-wise FR learning network for image patch matching, which transforms the image patch matching task into an image relationship-based pattern classification problem and dramatically improves generalization performances on image matching. Meanwhile, the proposed element-wise learning methods encourage full interaction between feature information and can naturally learn FR. Moreover, we propose to aggregate FR from multilevels, which integrates the multiscale FR for more precise matching. Experimental results demonstrate that our proposal achieves superior performances on cross-spectral image patch matching and single spectral image patch matching, and good generalization on image patch retrieval.
Fichier non déposé

Dates et versions

hal-03932827 , version 1 (10-01-2023)

Identifiants

Citer

Dou Quan, Shuang Wang, Ning Huyan, Jocelyn Chanussot, Ruojing Wang, et al.. Element-Wise Feature Relation Learning Network for Cross-Spectral Image Patch Matching. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33 (8), pp.3372-3386. ⟨10.1109/TNNLS.2021.3052756⟩. ⟨hal-03932827⟩
32 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More