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

Color Vision

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Cartesian Vector Notation

Cartesian Vector Notation

Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...

Non-Verbal Cues

Non-Verbal Cues

Non-verbal communication extends beyond gestures and facial expressions to include vocal elements known as paralanguage. Paralanguage consists of non-verbal vocal cues such as pitch, loudness, speech rate, pauses, and non-verbal vocalizations like laughter, sighs, and moans. These elements not only accompany speech but also provide critical emotional and contextual information.The Role of Paralanguage in CommunicationParalanguage adds depth to spoken language by conveying emotions and...

Coefficient of Variation

Coefficient of Variation

The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...

You might also read

Related Articles

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

Sort by

Same author

Is naloxone where it needs to be? Using spatial analytics to examine equitable distribution of community-based naloxone sites.

Journal of substance use and addiction treatment·2024

Same author

Adversarial Training Based Domain Adaptation of Skin Cancer Images.

Life (Basel, Switzerland)·2024

Same author

Natural language processing of online support group postings reveals patients' perspectives on strategies for managing psoriasis.

Dermatology reports·2024

Same author

Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.

Cureus·2023

Same author

Treatment options and emotional well-being in patients with rosacea: An unsupervised machine learning analysis of over 200,000 posts.

JAAD international·2023

Same author

Use of Sensors for Fall Prediction in Older Persons: A Scoping Review.

Computers, informatics, nursing : CIN·2023

Search research articles

Related Experiment Video

Updated: May 20, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Co.Vi.Wo.: Color Visual Words Based on Non-Predefined Size Codebooks.

Savvas A Chatzichristofis, Chryssanthi Iakovidou, Yiannis Boutalis

IEEE Transactions on Cybernetics

|July 10, 2012

Summary

This summary is machine-generated.

This study introduces a novel method for image retrieval using a self-organizing neural network to optimize codebook size for bag-of-visual-words (BOVW) models. The new color VWs descriptor significantly enhances retrieval accuracy and effectiveness.

More Related Videos

Training Synesthetic Letter-color Associations by Reading in Color

Training Synesthetic Letter-color Associations by Reading in Color

Published on: February 20, 2014

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Related Experiment Videos

Last Updated: May 20, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Training Synesthetic Letter-color Associations by Reading in Color

Training Synesthetic Letter-color Associations by Reading in Color

Published on: February 20, 2014

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Area of Science:

Computer Science
Information Technology
Artificial Intelligence

Background:

The increasing volume of multimedia data necessitates advanced content-based image retrieval (CBIR) techniques.
Bag-of-visual-words (BOVW) models offer semantic retrieval but are sensitive to codebook size determination.
Current BOVW methods face challenges in optimizing codebook size, impacting retrieval effectiveness.

Purpose of the Study:

To develop an automated method for determining the optimal codebook size in BOVW models.
To propose a novel descriptor, color VWs, for improved semantic image retrieval.
To enhance the precision and recall of content-based image retrieval systems.

Main Methods:

Utilized a self-growing and self-organized neural gas network for automatic codebook size calculation.
Introduced a soft-weighting technique for classifying local features (LF) into visual words (VW) with participation degrees.
Combined automated VW detection, soft-weighting, and color information extraction to create the color VWs descriptor.

Main Results:

The proposed color VWs descriptor demonstrated superior performance compared to 15 contemporary methods on benchmark datasets.
Achieved significant improvements in precision at K and the ability to retrieve the complete ground truth.
The automated codebook size determination method proved effective for diverse databases.

Conclusions:

The developed technique offers an efficient and effective solution for optimizing BOVW codebook sizes.
The color VWs descriptor represents a significant advancement in semantic image retrieval accuracy.
This research contributes to more robust and precise visual content retrieval systems.