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

Histogram

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...

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.

IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

You might also read

Related Articles

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

Sort by

Same author

Padé Neurons for Efficient Neural Models.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

National Consensus Statement on Role of Bisoprolol across Cardiovascular Continuum: Special Focus on Women.

The Journal of the Association of Physicians of India..·2025

Same author

End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021

Same author

Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020

Same author

Two-dimensional mesh-based mosaic representation for manipulation of video objects with occlusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same author

Temporal segmentation of video objects for hierarchical object-based motion description.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Robust color histogram descriptors for video segment retrieval and identification.

A Müfit Ferman¹, A Murat Tekalp, Rajiv Mehrotra

¹Department of Electrical and Computer Engineering, University of Rochester, NY 14627-0126, USA. mferman@sharplabs.com

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

|February 5, 2008

Summary

This study introduces robust histogram-based color descriptors for video analysis. Alpha-trimmed average histograms outperform traditional methods, enhancing video segment retrieval and color representation.

More Related Videos

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Jul 7, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

Computer Vision
Image Processing
Multimedia Systems

Background:

Representing color features across multiple video frames (GoF) is crucial for visual information management but challenging.
Key frame-based methods often yield unreliable results due to dependency on representative frame selection.
Existing methods struggle with variations like brightness changes, occlusion, and editing effects.

Purpose of the Study:

To develop reliable and efficient histogram-based color descriptors for representing color properties of multiple images or GoF.
To introduce alpha-trimmed average histograms and intersection histograms for improved color feature representation.
To enhance video segment retrieval and query frame identification.

Main Methods:

Developed alpha-trimmed average histograms by filtering individual frame histograms to create robust color representations.
Introduced the intersection histogram to identify common pixel colors across all frames in a GoF.
Applied these descriptors to video segment retrieval and query frame identification algorithms.

Main Results:

Alpha-trimmed average histograms demonstrated superior performance over key frame-based methods in video segment retrieval.
The intersection histogram enabled a fast and efficient algorithm for query frame belonging identification.
Proposed descriptors were validated and included in the ISO standard MPEG-7.

Conclusions:

Histogram-based color descriptors offer a robust and efficient solution for representing color features in visual information management.
The developed methods significantly improve the accuracy and reliability of video analysis tasks.
Inclusion in the MPEG-7 standard highlights the practical significance and effectiveness of these descriptors.