排名(信息检索)
计算机科学
特征(语言学)
图形
人工智能
情报检索
多重图
模式识别(心理学)
图像(数学)
理论计算机科学
语言学
哲学
作者
Shenglan Liu,Miao Sun,Hong Qiao,Shuyuan Chen,Yang Liu
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2021-03-01
卷期号:32 (3): 1389-1399
被引量:8
标识
DOI:10.1109/tnnls.2020.2984676
摘要
A single feature is hard to describe the content of images from an overall perspective, which limits the retrieval performances of single-feature-based methods in image retrieval tasks. To fully describe the properties of images and improve the retrieval performances, multifeature fusion ranking-based methods are proposed. However, the effectiveness of multifeature fusion in image retrieval has not been theoretically explained. This article gives a theoretical proof to illustrate the role of independent features in improving the retrieval results. Based on the theoretical proof, the original ranking list generated with a single feature greatly influences the performances of multifeature fusion ranking. Inspired by the principle of three degrees of influence in social networks, this article proposes a reranking method named k -nearest neighbors' neighbors' neighbors' graph (N3G) to improve the original ranking list by a single feature. Furthermore, a multigraph fusion ranking (MFR) method motivated by the group relation theory in social networks for multifeature ranking is also proposed, which considers the correlations of all images in multiple neighborhood graphs. Evaluation experiments conducted on several representative data sets (e.g., UK-bench, Holiday, Corel-10K, and Cifar-10) validate that N3G and MFR outperform the other state-of-the-art methods.
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