.png){kind=icon}
返回
Collaborative Image Relevance Learning for Visual Re-Ranking
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2020.3029886.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In content-based image retrieval, the initial retrieval result may be unsatisfactory, which can be refined with visual re-ranking techniques, such as query expansion, geometric verification, etc. In this work, we approach visual re-ranking from a novel perspective. Observing that the contextual similarity of images from a retrieval result list exhibits strong visual relevance, we propose to collaboratively learn the semantic relevance among images for visual re-ranking. In our approach, we represent the image set of a fixed-length retrieval list into a correlation matrix, and learn the relevance of all image pairs simultaneously with a lightweight CNN model. To optimize the CNN model, a weighted MSE loss is defined, which takes into account the sparsity of labels. To find the optimal length of retrieval result list for different queries, we present a query sensitive selection method. We conduct comprehensive experiments on five benchmark datasets, and demonstrate the generality, and effectiveness of the proposed visual re-ranking method.
Keyword:
Visualization
Image retrieval
Computational modeling
Correlation
Feature extraction
Semantics
Deep learning
Image retrieval
re-ranking
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.7
论文数:
4.5K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容