散列函数
模式识别(心理学)
相似性(几何)
计算机科学
水准点(测量)
人工智能
公制(单位)
局部敏感散列
图像检索
一致性(知识库)
最近邻搜索
特征哈希
特征提取
哈希表
数据挖掘
双重哈希
图像(数学)
运营管理
计算机安全
大地测量学
经济
地理
作者
Yang Shi,Xiushan Nie,Xingbo Liu,Li Zou,Yilong Yin
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:31: 2755-2766
被引量:15
标识
DOI:10.1109/tip.2022.3158092
摘要
Compact hash codes can facilitate large-scale multimedia retrieval, significantly reducing storage and computation. Most hashing methods learn hash functions based on the data similarity matrix, which is predefined by supervised labels or a distance metric type. However, this predefined similarity matrix cannot accurately reflect the real similarity relationship among images, which results in poor retrieval performance of hashing methods, especially in multi-label datasets and zero-shot datasets that are highly dependent on similarity relationships. Toward this end, this study proposes a new supervised hashing method called supervised adaptive similarity matrix hashing (SASH) via feature-label space consistency. SASH not only learns the similarity matrix adaptively, but also extracts the label correlations by maintaining consistency between the feature and the label space. This correlation information is then used to optimize the similarity matrix. The experiments on three large normal benchmark datasets (including two multi-label datasets) and three large zero-shot benchmark datasets show that SASH has an excellent performance compared with several state-of-the-art techniques.
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