.png){kind=icon}
Return
Multi-View Image Classification With Visual, Semantic and View Consistency
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2019.2934576.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-view visual classification methods have been widely applied to use discriminative information of different views. This strategy has been proven very effective by many researchers. On the one hand, images are often treated independently without fully considering their visual and semantic correlations. On the other hand, view consistency is often ignored. To solve these problems, in this paper, we propose a novel multi-view image classification method with visual, semantic and view consistency (VSVC). For each image, we linearly combine multi-view information for image classification. The combination parameters are determined by considering both the classification loss and the visual, semantic and view consistency. Visual consistency is imposed by ensuring that visually similar images of the same view are predicted to have similar values. For semantic consistency, we impose the locality constraint that nearby images should be predicted to have the same class by multi-view combination. View consistency is also used to ensure that similar images have consistent multi-view combination parameters. An alternative optimization strategy is used to learn the combination parameters. To evaluate the effectiveness of VSVC, we perform image classification experiments on several public datasets. The experimental results on these datasets show the effectiveness of the proposed VSVC method.
Keywords:
Semantics
Visualization
Correlation
Training
Internet
Encoding
Image representation
Multi-view learning
image classification
visual consistency
semantic consistency
view consistency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
View more organizations
Cited Papers
ShareSave
ShareSaveMICAL2is a novel human cancer gene controlling mesenchymal to epithelial transition involved in cancer growth and invasion.png)
Oncotarget
OAAI
OncotargetIF0
OAAIShareSaveShareSaveShareSaveShareSaveLearning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-Based Tracker
PREAI
ShareSave View more