CN114219049B - 一种基于层级约束的细粒度笔石图像分类方法和装置 - Google Patents
一种基于层级约束的细粒度笔石图像分类方法和装置 Download PDFInfo
- Publication number
- CN114219049B CN114219049B CN202210159814.4A CN202210159814A CN114219049B CN 114219049 B CN114219049 B CN 114219049B CN 202210159814 A CN202210159814 A CN 202210159814A CN 114219049 B CN114219049 B CN 114219049B
- Authority
- CN
- China
- Prior art keywords
- stone
- images
- stroke
- image
- loss
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/761—Proximity, similarity or dissimilarity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/24—Aligning, centring, orientation detection or correction of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
- G06V10/7635—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks based on graphs, e.g. graph cuts or spectral clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (9)
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210159814.4A CN114219049B (zh) | 2022-02-22 | 2022-02-22 | 一种基于层级约束的细粒度笔石图像分类方法和装置 |
US18/147,019 US11804029B2 (en) | 2022-02-22 | 2022-12-28 | Hierarchical constraint (HC)-based method and system for classifying fine-grained graptolite images |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210159814.4A CN114219049B (zh) | 2022-02-22 | 2022-02-22 | 一种基于层级约束的细粒度笔石图像分类方法和装置 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN114219049A CN114219049A (zh) | 2022-03-22 |
CN114219049B true CN114219049B (zh) | 2022-05-10 |
Family
ID=80709162
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210159814.4A Active CN114219049B (zh) | 2022-02-22 | 2022-02-22 | 一种基于层级约束的细粒度笔石图像分类方法和装置 |
Country Status (2)
Country | Link |
---|---|
US (1) | US11804029B2 (zh) |
CN (1) | CN114219049B (zh) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115081489A (zh) * | 2022-07-13 | 2022-09-20 | 重庆大学 | 基于小波分解矩阵和残差网络的时间序列分类方法 |
CN116824306B (zh) * | 2023-08-28 | 2023-11-17 | 天津大学 | 基于多模态元数据的笔石化石图像识别模型的训练方法 |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107346328A (zh) * | 2017-05-25 | 2017-11-14 | 北京大学 | 一种基于多粒度层级网络的跨模态关联学习方法 |
CN108009286A (zh) * | 2017-12-25 | 2018-05-08 | 合肥阿巴赛信息科技有限公司 | 一种基于深度学习的草图检索方法 |
Family Cites Families (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IL236598A0 (en) * | 2015-01-05 | 2015-05-31 | Superfish Ltd | Image similarity as a function of image weighted image descriptors generated from neural networks |
CN104751193B (zh) * | 2015-04-24 | 2018-02-13 | 中国矿业大学(北京) | 基于距离约束相似性的煤岩识别方法 |
US11503757B2 (en) * | 2017-12-03 | 2022-11-22 | Seedx Technologies Inc. | Systems and methods for sorting of seeds |
CN110414299B (zh) * | 2018-04-28 | 2024-02-06 | 中山大学 | 一种基于计算机视觉的猴脸亲缘关系分析方法 |
CN109359684B (zh) * | 2018-10-17 | 2021-10-29 | 苏州大学 | 基于弱监督定位和子类别相似性度量的细粒度车型识别方法 |
WO2020138479A1 (ja) * | 2018-12-28 | 2020-07-02 | 国立大学法人大阪大学 | 個体の形質情報を予測するためのシステムまたは方法 |
CN109858521B (zh) * | 2018-12-29 | 2021-01-01 | 国际竹藤中心 | 一种基于人工智能深度学习的竹子种类识别方法 |
CN110413924B (zh) * | 2019-07-18 | 2020-04-17 | 广东石油化工学院 | 一种半监督多视图学习的网页分类方法 |
CN111079526A (zh) * | 2019-11-07 | 2020-04-28 | 中央财经大学 | 一种信鸽亲缘关系分析方法、装置及存储介质 |
CN111553193B (zh) * | 2020-04-01 | 2022-11-11 | 东南大学 | 一种基于轻量级深层神经网络的视觉slam闭环检测方法 |
US20210365745A1 (en) * | 2020-08-10 | 2021-11-25 | Bp Corporation North America Inc. | Method and Apparatus for Implementing Automated Fossil Identification to Augment Biostratigraphy Workflows |
KR102454715B1 (ko) * | 2021-08-10 | 2022-10-17 | 인트플로우 주식회사 | 영상에 기반하여 동물의 승가 행위를 검출하는 장치 및 방법 |
CN113657492A (zh) * | 2021-08-17 | 2021-11-16 | 上海海事大学 | 一种笔石化石图像的分类方法 |
US20230077353A1 (en) * | 2021-08-31 | 2023-03-16 | University Of South Florida | Systems and Methods for Classifying Mosquitoes Based on Extracted Masks of Anatomical Components from Images |
CN114049535A (zh) * | 2021-11-16 | 2022-02-15 | 昆明理工大学 | 基于多尺度和非压缩激励通道注意力的野外蝴蝶识别方法 |
-
2022
- 2022-02-22 CN CN202210159814.4A patent/CN114219049B/zh active Active
- 2022-12-28 US US18/147,019 patent/US11804029B2/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107346328A (zh) * | 2017-05-25 | 2017-11-14 | 北京大学 | 一种基于多粒度层级网络的跨模态关联学习方法 |
CN108009286A (zh) * | 2017-12-25 | 2018-05-08 | 合肥阿巴赛信息科技有限公司 | 一种基于深度学习的草图检索方法 |
Also Published As
Publication number | Publication date |
---|---|
CN114219049A (zh) | 2022-03-22 |
US11804029B2 (en) | 2023-10-31 |
US20230267703A1 (en) | 2023-08-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Niu et al. | Single image super-resolution via a holistic attention network | |
Ertosun et al. | Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks | |
Li et al. | HEp-2 specimen image segmentation and classification using very deep fully convolutional network | |
Liu et al. | Panoptic feature fusion net: a novel instance segmentation paradigm for biomedical and biological images | |
Wan et al. | Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement | |
CN114219049B (zh) | 一种基于层级约束的细粒度笔石图像分类方法和装置 | |
CN109711448A (zh) | 基于判别关键域和深度学习的植物图像细粒度分类方法 | |
Kashyap | Breast cancer histopathological image classification using stochastic dilated residual ghost model | |
CN110633708A (zh) | 一种基于全局模型和局部优化的深度网络显著性检测方法 | |
CN111652273B (zh) | 一种基于深度学习的rgb-d图像分类方法 | |
Vallet et al. | A multi-label convolutional neural network for automatic image annotation | |
Li et al. | Recent advances of machine vision technology in fish classification | |
CN112686902A (zh) | 核磁共振影像中脑胶质瘤识别与分割的两阶段计算方法 | |
Zhou et al. | Attention transfer network for nature image matting | |
Nasab et al. | Deep learning in spatially resolved transcriptomics: a comprehensive technical view | |
Meng et al. | Residual dense asymmetric convolutional neural network for hyperspectral image classification | |
Da Xu et al. | Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm | |
CN111401122B (zh) | 一种基于知识分类的复杂目标渐近识别方法及装置 | |
Chen et al. | Automatic identification of commodity label images using lightweight attention network | |
Peng et al. | Fully convolutional neural networks for tissue histopathology image classification and segmentation | |
Li et al. | A concatenating framework of shortcut convolutional neural networks | |
Yang et al. | Automatically adjustable multi-scale feature extraction framework for hyperspectral image classification | |
Song | A More Efficient Approach for Remote Sensing Image Classification. | |
Yang et al. | Multi-level contour combination features for shape recognition | |
Yan et al. | Two and multiple categorization of breast pathological images by transfer learning |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CB03 | Change of inventor or designer information |
Inventor after: Xu Honghe Inventor after: Niu Zhibin Inventor after: Pan Yaohua Inventor before: Pan Yaohua Inventor before: Xu Honghe Inventor before: Niu Zhibin |
|
CB03 | Change of inventor or designer information | ||
CP03 | Change of name, title or address |
Address after: No.39, Beijing East Road, Xuanwu District, Nanjing City, Jiangsu Province, 210008 Patentee after: NANJING INST. OF GEOLOGY AND PALEONTOLOGY, CHINESE ACADEMY OF SCIENCES Patentee after: Tianjin University Address before: 300072 Tianjin City, Nankai District Wei Jin Road No. 92 Patentee before: Tianjin University Patentee before: NANJING INST. OF GEOLOGY AND PALEONTOLOGY, CHINESE ACADEMY OF SCIENCES |
|
CP03 | Change of name, title or address |