Search research articles

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

Related Concept Videos

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

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

Sort by

Same author

Role of spin-orbit coupling for the band splitting in ⍺-Sb and ⍺-Bi on SiC(0001).

Journal of physics. Condensed matter : an Institute of Physics journal·2025

Same author

Characterization of integrative and conjugative elements carrying erm(B) and tet(O) resistance determinants in streptococcus uberis isolates from bovine milk in Chiba prefecture, Japan: CompArative GEne cluster analysis toolbox with ICEfinder.

BMC research notes·2024

Same author

Streptococcus canis transcriptomic modifications in host cell entry environments of human keratinocytes.

BMC genomics·2024

Same author

One-step syntheses of diaza-dioxa-fenestranes via the sequential (3 + 2) cycloadditions of linear precursors and their structural analyses.

Nature communications·2024

Same author

Automated magnetic resonance imaging-based grading of the lumbar intervertebral disc and facet joints.

JOR spine·2024

Same author

Seven tesla knee MRI T2*-mapping detects intrasubstance meniscus degeneration in patients with posterior root tears.

Radiology advances·2024

Search research articles

Related Experiment Video

Updated: Apr 4, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Mixture of Subspaces Image Representation and Compact Coding for Large-Scale Image Retrieval.

Takashi Takahashi, Takio Kurita

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 10, 2015

Summary

This study introduces an asymmetric approach for content-based image retrieval, modeling local descriptor distributions for database images. This method achieves high accuracy and low memory cost, outperforming existing techniques.

More Related Videos

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Related Experiment Videos

Last Updated: Apr 4, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

Computer Science
Information Retrieval
Machine Learning

Background:

Content-based image retrieval (CBIR) commonly uses local descriptors.
Descriptor-by-descriptor matching offers high accuracy but requires significant memory.
Global image representations reduce memory but often sacrifice retrieval accuracy.

Purpose of the Study:

To develop a novel CBIR approach balancing high retrieval accuracy with low memory footprint.
To investigate an asymmetric method for efficient and effective image representation.

Main Methods:

An asymmetric approach modeling probability distributions of local descriptors for database images.
Utilizing a mixture model of probabilistic principal component analysis (PPCA) for global image representation.
Employing a likelihood function for computing matching scores and an encoding algorithm for compact representation.

Main Results:

The proposed method achieves high retrieval accuracy comparable to or exceeding state-of-the-art techniques.
Database images are represented using less than several hundred bytes, significantly reducing memory requirements.
The asymmetric approach demonstrates superior performance over methods like Fisher vectors.

Conclusions:

The asymmetric modeling of local descriptor distributions offers an effective solution for large-scale CBIR.
This method successfully combines low memory cost with high retrieval accuracy.
The PPCA-based representation and encoding algorithm provide a promising direction for future CBIR research.