Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ranks01:02

Ranks

578
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
578
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

893
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
893
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

575
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
575
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.6K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.6K
X-ray Imaging01:24

X-ray Imaging

11.1K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
11.1K
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

14.8K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
14.8K

You might also read

Related Articles

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

Sort by
Same author

Toward Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge.

IEEE transactions on medical imagingยท2025
Same author

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023.

Medical image analysisยท2025
Same author

Classification of lung cancer subtypes on CT images with synthetic pathological priors.

Medical image analysisยท2024
Same author

A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conferenceยท2023
Same author

Automatic Lung Cancer Subtypes Classification on CT Images with Self-generated Multi-modality Hybrid Features.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conferenceยท2023
Same author

Publisher Correction: Mining multi-center heterogeneous medical data with distributed synthetic learning.

Nature communicationsยท2023

Related Experiment Video

Updated: Apr 4, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Query Specific Rank Fusion for Image Retrieval.

Shaoting Zhang, Ming Yang, Timothee Cour

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This study introduces a novel graph-based approach to fuse image retrieval results from different algorithms. By merging retrieval ranks, it adaptively enhances search precision for visually similar images without needing supervision.

    More Related Videos

    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    11.3K

    Related Experiment Videos

    Last Updated: Apr 4, 2026

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.1K
    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    11.3K

    Area of Science:

    • Computer Science
    • Information Retrieval
    • Image Processing

    Background:

    • Scalable image retrieval relies on local features (e.g., vocabulary trees) and holistic features (e.g., hashing codes).
    • Individual methods show variable precision, motivating fusion for improved performance.
    • Feature-level fusion is challenging due to differing algorithm characteristics.

    Purpose of the Study:

    • To develop a method for fusing ordered retrieval sets (image ranks) from multiple algorithms.
    • To boost image retrieval precision without compromising scalability.
    • To create a query-specific fusion approach adaptable to different retrieval strengths.

    Main Methods:

    • Modeling retrieval ranks as graphs of candidate images.
    • Proposing a graph-based, query-specific fusion by merging graphs and reranking via link analysis.
    • On-the-fly measurement of retrieval quality using nearest neighborhood consistency.

    Main Results:

    • The proposed method adaptively integrates strengths of local and holistic feature retrieval.
    • Achieved competitive performance across four public datasets (UKbench, Corel-5K, Holidays, San Francisco Landmarks).
    • Demonstrated state-of-the-art results on several datasets, including an N-S score of 3.83 for UKbench.

    Conclusions:

    • The graph-based fusion approach effectively enhances image retrieval precision.
    • The method is unsupervised, parameter-light, and easy to implement.
    • It offers a scalable solution for combining diverse image retrieval techniques.