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

Probability Histograms01:17

Probability Histograms

13.8K
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.
13.8K
Histogram01:05

Histogram

18.9K
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...
18.9K
Relative Frequency Histogram01:14

Relative Frequency Histogram

6.8K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
6.8K
Binomial Probability Distribution01:15

Binomial Probability Distribution

16.7K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
16.7K
Probability Distributions01:32

Probability Distributions

13.4K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
13.4K
Ratio Level of Measurement00:54

Ratio Level of Measurement

22.5K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
22.5K

You might also read

Related Articles

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

Sort by
Same author

GEOMETRY OF LONG-TAILED REPRESENTATION LEARNING: REBALANCING FEATURES FOR SKEWED DISTRIBUTIONS.

... International Conference on Learning Representations·2026
Same author

Setup-Independent Full Projector Compensation.

IEEE transactions on visualization and computer graphics·2026
Same author

DiffPC: Diffusion-Based Projector Photometric Compensation.

IEEE transactions on visualization and computer graphics·2026
Same author

Probing uric acid-related prognostic genes and their molecular mechanisms in prostate cancer based on transcriptomic data.

Discover oncology·2026
Same author

Allicin suppressed bladder cancer cell biological activities via regulation of the miR-26b-5p/PTEN axis in an <i>in vitro</i> study.

Archives of medical science : AMS·2026
Same author

Cortical layer multi-parameter analysis of neurovascular impairments in AD/ADRD rodent model with in vivo optical imaging.

Translational neurodegeneration·2025

Related Experiment Video

Updated: Apr 4, 2026

How to Create and Use Binocular Rivalry
14:34

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

77.1K

Bin Ratio-Based Histogram Distances and Their Application to Image Classification.

Weiming Hu, Nianhua Xie, Ruiguang Hu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel bin ratio-based histogram distance (BRD) to address background variations in image classification. The BRD method improves robustness against partial matching and normalization issues, enhancing image analysis accuracy.

    More Related Videos

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.8K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.3K

    Related Experiment Videos

    Last Updated: Apr 4, 2026

    How to Create and Use Binocular Rivalry
    14:34

    How to Create and Use Binocular Rivalry

    Published on: November 10, 2010

    77.1K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.8K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.3K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Histogram-based image representations face challenges with background variations, leading to partial matching and normalization problems.
    • Traditional histogram distances struggle with significant bin differences and normalization-induced changes.

    Purpose of the Study:

    • To develop a robust histogram distance metric resilient to background variations and partial matching.
    • To introduce a novel intra-cross-bin distance measure that captures inter-bin correlations efficiently.

    Main Methods:

    • Proposed a bin ratio-based histogram distance (BRD) using ratios of bin values instead of differences.
    • Combined BRD with ℓ1 and χ(2) distances to create ℓ1 BRD and χ(2) BRD, enhancing robustness to noise.
    • Developed a method to assess histogram distance robustness to partial matching.

    Main Results:

    • BRD demonstrated robustness to partial matching and histogram normalization on synthetic datasets.
    • Combined BRD metrics (ℓ1 BRD, χ(2) BRD) showed promising results in image classification tasks on benchmark datasets.
    • The proposed methods effectively captured correlations between histogram bins with linear computational complexity.

    Conclusions:

    • The bin ratio-based histogram distance (BRD) offers a robust solution for histogram comparison under challenging conditions.
    • BRD and its combinations provide improved performance in image classification compared to traditional methods.
    • The developed approach enhances the reliability of histogram-based image analysis and classification.