CN112528065B - 一种流形相似度保持自编码器的医学超声图像检索方法 - Google Patents
一种流形相似度保持自编码器的医学超声图像检索方法 Download PDFInfo
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CN113449849B (zh) * | 2021-06-29 | 2022-05-27 | 桂林电子科技大学 | 基于自编码器的学习型文本哈希方法 |
CN114022701B (zh) * | 2021-10-21 | 2022-06-24 | 南京审计大学 | 基于近邻监督离散判别哈希的图像分类方法 |
CN116610927B (zh) * | 2023-07-21 | 2023-10-13 | 傲拓科技股份有限公司 | 基于fpga的风机齿轮箱轴承故障诊断方法及诊断模块 |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104820696A (zh) * | 2015-04-29 | 2015-08-05 | 山东大学 | 一种基于多标签最小二乘哈希算法的大规模图像检索方法 |
CN105069173A (zh) * | 2015-09-10 | 2015-11-18 | 天津中科智能识别产业技术研究院有限公司 | 基于有监督的拓扑保持哈希的快速图像检索方法 |
CN106126585A (zh) * | 2016-06-20 | 2016-11-16 | 北京航空航天大学 | 基于质量分级与感知哈希特征组合的无人机图像检索方法 |
CN106780639A (zh) * | 2017-01-20 | 2017-05-31 | 中国海洋大学 | 基于显著性特征稀疏嵌入和极限学习机的哈希编码方法 |
CN108182256A (zh) * | 2017-12-31 | 2018-06-19 | 厦门大学 | 一种基于离散局部线性嵌入哈希的高效图像检索方法 |
CN109166615A (zh) * | 2018-07-11 | 2019-01-08 | 重庆邮电大学 | 一种随机森林哈希的医学ct图像存储与检索方法 |
CN109783682A (zh) * | 2019-01-19 | 2019-05-21 | 北京工业大学 | 一种基于点对相似度的深度非松弛哈希图像检索方法 |
CN110069644A (zh) * | 2019-04-24 | 2019-07-30 | 南京邮电大学 | 一种基于深度学习的压缩域大规模图像检索方法 |
CN110083734A (zh) * | 2019-04-15 | 2019-08-02 | 中南大学 | 基于自编码网络和鲁棒核哈希的半监督图像检索方法 |
CN110516095A (zh) * | 2019-08-12 | 2019-11-29 | 山东师范大学 | 基于语义迁移的弱监督深度哈希社交图像检索方法和*** |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150169682A1 (en) * | 2013-10-18 | 2015-06-18 | Google Inc. | Hash Learning |
CN104298791A (zh) * | 2014-11-19 | 2015-01-21 | 中国石油大学(华东) | 一种基于集成哈希编码的快速图像检索方法 |
US20170293838A1 (en) * | 2016-04-06 | 2017-10-12 | Nec Laboratories America, Inc. | Deep high-order exemplar learning for hashing and fast information retrieval |
CN106777038B (zh) * | 2016-12-09 | 2019-06-14 | 厦门大学 | 一种基于序列保留哈希的超低复杂度图像检索方法 |
-
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- 2020-12-17 CN CN202011496971.1A patent/CN112528065B/zh active Active
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104820696A (zh) * | 2015-04-29 | 2015-08-05 | 山东大学 | 一种基于多标签最小二乘哈希算法的大规模图像检索方法 |
CN105069173A (zh) * | 2015-09-10 | 2015-11-18 | 天津中科智能识别产业技术研究院有限公司 | 基于有监督的拓扑保持哈希的快速图像检索方法 |
CN106126585A (zh) * | 2016-06-20 | 2016-11-16 | 北京航空航天大学 | 基于质量分级与感知哈希特征组合的无人机图像检索方法 |
CN106780639A (zh) * | 2017-01-20 | 2017-05-31 | 中国海洋大学 | 基于显著性特征稀疏嵌入和极限学习机的哈希编码方法 |
CN108182256A (zh) * | 2017-12-31 | 2018-06-19 | 厦门大学 | 一种基于离散局部线性嵌入哈希的高效图像检索方法 |
CN109166615A (zh) * | 2018-07-11 | 2019-01-08 | 重庆邮电大学 | 一种随机森林哈希的医学ct图像存储与检索方法 |
CN109783682A (zh) * | 2019-01-19 | 2019-05-21 | 北京工业大学 | 一种基于点对相似度的深度非松弛哈希图像检索方法 |
CN110083734A (zh) * | 2019-04-15 | 2019-08-02 | 中南大学 | 基于自编码网络和鲁棒核哈希的半监督图像检索方法 |
CN110069644A (zh) * | 2019-04-24 | 2019-07-30 | 南京邮电大学 | 一种基于深度学习的压缩域大规模图像检索方法 |
CN110516095A (zh) * | 2019-08-12 | 2019-11-29 | 山东师范大学 | 基于语义迁移的弱监督深度哈希社交图像检索方法和*** |
Non-Patent Citations (2)
Title |
---|
Hashing with Non-Linear Manifold Learning;Yanzhen Liu等;《IEEE》;20161226;第1-8页 * |
基于PCA的哈希图像检索算法;马绍覃;《计算机工程与设计》;20200216;第483-487页 * |
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