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Angular Quantization Online Hashing for Image Retrieval
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DOI：10.1109/ACCESS.2021.3095367.png)
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摘要
En 中文
Online hash method with fast search mechanism and compact index structure plays a pivotal role. The inner product between label data has become one of the important means to measure the similarity between existing data and new data streams in online hashing methods. However, due to its discrete attributes and semantic gap, it often leads to a large amount of information loss. In this article, we propose a novel method called Angular Quantization Online Hashing (AQOH) to focus on learning compact binary codes with the help of cosine distance. Specifically, we propose an online hashing method for angular quantization, by minimizing the quantization error between the cosine similarity calculated from the original data and the generated binary code between the existing data and the new data stream. Further, within this framework, two effective algorithms to complete the optimization of the objective function to be designed, including continuous and discrete methods, respectively. Extensive experiments on various benchmark databases for online retrieval verify that our method outperforms many state-of-the art learning to hash methods.
Keyword:
Binary codes
Task analysis
Feature extraction
Semantics
Quantization (signal)
Optimization
Image retrieval
Online hashing
image retrieval
cosine similarity
unsupervised
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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:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
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The future of imaging: developing the tools for monitoring response to therapy in oncology: the 2009 Sir James MacKenzie Davidson Memorial lecture影像学的未来: 开发监测肿瘤学治疗反应的工具: 2009詹姆斯·麦肯齐·戴维森爵士纪念讲座OAAI
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