Search research articles

Related Concept Videos

Complementary DNA

Complementary DNA

Retrieval

Retrieval

Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...

ER Retrieval Pathway

ER Retrieval Pathway

In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...

Location and Orientation of the Heart

Location and Orientation of the Heart

The human heart, despite its modest size and weight, is an organ of remarkable strength and endurance. Roughly the size of a fist, the heart weighs between 250 and 350 grams and is nestled within the mediastinum, the medial cavity of the thorax. It extends obliquely for about 12 to 14 cm, resting on the superior surface of the diaphragm. The heart is positioned anterior to the vertebral column and posterior to the sternum, with two-thirds of its mass lying to the left of the midsternal line.

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Methods of Documentation I: Source-Oriented Records

Methods of Documentation I: Source-Oriented Records

Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The CXCL9/SPP1 polarity axis in tumor-associated macrophages: immunoregulatory and prognostic significance in non-small cell lung cancer.

Frontiers in immunology·2026

Same author

Subspecialty-specific foundation model for intelligent gastrointestinal pathology.

NPJ digital medicine·2026

Same author

Multi-omics integration reveals that pyrimidine metabolism in lung adenocarcinoma drives an immunosuppressive microenvironment.

iScience·2026

Same author

Molecular classification and prognosis study of pancreatic ductal adenocarcinoma through multi-omics integrated clustering analysis.

PeerJ·2026

Same author

Next Bit Prediction: A Unified Lossless and Lossy Point Cloud Geometry Compression Framework.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Glycosylated IgG antibodies contribute to the recovery of haemorrhagic fever with renal syndrome patients.

eLife·2025

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Feb 8, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Retrieval Oriented Deep Feature Learning With Complementary Supervision Mining.

Yue Lv, Wengang Zhou, Qi Tian

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 10, 2018

Summary

This summary is machine-generated.

This study integrates Scale-Invariant Feature Transform (SIFT) features into deep convolutional neural networks (CNNs) for improved image retrieval. The novel approach enhances CNN features, leading to more effective instance retrieval performance.

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Related Experiment Videos

Last Updated: Feb 8, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Area of Science:

Computer Vision
Machine Learning
Image Retrieval

Background:

Deep convolutional neural networks (CNNs) excel in various computer vision tasks.
Standard CNN features show promise for image retrieval but lack specificity for instance-level tasks.
Scale-Invariant Feature Transform (SIFT) features are effective in image retrieval but are low-level.

Purpose of the Study:

To develop a learning-based method for embedding SIFT features into CNN features for instance retrieval.
To create CNN features that combine semantic understanding with SIFT's retrieval-oriented properties.

Main Methods:

A Siamese network structure was employed to learn CNN features.
A novel learning objective incorporated both similarity loss and fidelity loss.
Similarity loss integrates SIFT's neighborhood structure; fidelity loss preserves original CNN feature characteristics.

Main Results:

The proposed method successfully embeds SIFT feature properties into CNN features.
Experimental evaluations on public datasets demonstrate the effectiveness of the learned features for image retrieval.
The generated CNN features are well-oriented for instance retrieval tasks.

Conclusions:

The integration of SIFT features into CNNs via a Siamese structure significantly enhances image retrieval capabilities.
The proposed learning paradigm effectively combines semantic and low-level features for superior performance.
This approach offers a promising direction for advancing instance-level image retrieval.