CN106529601B - 基于稀疏子空间多任务学习的图像分类预测方法 - Google Patents
基于稀疏子空间多任务学习的图像分类预测方法 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 23
- 230000006870 function Effects 0.000 claims abstract description 15
- 238000012549 training Methods 0.000 claims abstract description 11
- 230000000007 visual effect Effects 0.000 claims abstract description 10
- 238000005457 optimization Methods 0.000 claims abstract description 9
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2431—Multiple classes
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- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
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Abstract
Description
Kodak | MSRA-MM | NUS-WIDE |
颜色相关图 | 颜色相关图 | 颜色相关图 |
共生矩阵纹理特征 | 边缘方向直方图 | 边缘方向直方图 |
边缘方向直方图 | 人脸特征 | 词袋特征 |
人脸特征 | 颜色直方图 | 颜色直方图 |
颜色直方图 | RGB颜色直方图 | 块颜色矩 |
块颜色矩 | 块颜色矩 | 小波纹理特征 |
小波纹理特征 | 小波纹理特征 |
Lasso | Group lasso | subspaceMTL | SGLSMTL | |
Kodak | 0.7148 | 0.7395 | 0.7866 | 0.8245 |
MSRA-MM | 0.6208 | 0.6239 | 0.6785 | 0.7022 |
NUS-WIDE | 0.7082 | 0.7154 | 0.7513 | 0.7816 |
Lasso | Group lasso | subspaceMTL | SGLSMTL | |
Kodak | 0.7192 | 0.7322 | 0.7854 | 0.8098 |
MSRA-MM | 0.7058 | 0.7171 | 0.7544 | 0.7832 |
NUS-WIDE | 0.7250 | 0.7334 | 0.7532 | 0.7793 |
Lasso | Group lasso | subspaceMTL | SGLSMTL | |
Kodak | 0.8839 | 0.8916 | 0.9188 | 0.9312 |
MSRA-MM | 0.8265 | 0.8470 | 0.9172 | 0.9411 |
NUS-WIDE | 0.8055 | 0.8317 | 0.8933 | 0.9122 |
Claims (3)
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Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
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CN109063732B (zh) * | 2018-06-26 | 2019-07-09 | 山东大学 | 基于特征交互和多任务学习的图像排序方法及*** |
CN113011438B (zh) * | 2021-03-16 | 2023-09-05 | 东北大学 | 基于节点分类和稀疏图学习的双模态图像显著性检测方法 |
CN113205150B (zh) * | 2021-05-21 | 2024-03-01 | 东北大学 | 一种基于多时相融合的多任务分类***及方法 |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102375855A (zh) * | 2010-08-20 | 2012-03-14 | 浙江大学 | 基于稀疏组群结构的图像标注方法 |
CN102930301A (zh) * | 2012-10-16 | 2013-02-13 | 西安电子科技大学 | 基于特征权重学习与核稀疏表示的图像分类方法 |
CN103440513A (zh) * | 2013-09-17 | 2013-12-11 | 西安电子科技大学 | 基于稀疏非负张量分解的大脑特定视觉认知状态判定方法 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
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US20150095490A1 (en) * | 2013-10-02 | 2015-04-02 | Nec Laboratories America, Inc. | Online sparse regularized joint analysis for heterogeneous data |
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Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102375855A (zh) * | 2010-08-20 | 2012-03-14 | 浙江大学 | 基于稀疏组群结构的图像标注方法 |
CN102930301A (zh) * | 2012-10-16 | 2013-02-13 | 西安电子科技大学 | 基于特征权重学习与核稀疏表示的图像分类方法 |
CN103440513A (zh) * | 2013-09-17 | 2013-12-11 | 西安电子科技大学 | 基于稀疏非负张量分解的大脑特定视觉认知状态判定方法 |
Non-Patent Citations (2)
Title |
---|
Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification;Xiaofeng Zhu 等;《IEEE Transactions on Biomedical Engineering》;20150811;第63卷(第3期);第607-618页 * |
融合异构特征的子空间迁移学习算法;张景祥 等;《自动化学报》;20140228;第40卷(第2期);第236-246页 * |
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