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Related Concept Videos

What is a Mode?01:07

What is a Mode?

The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
Skewness01:06

Skewness

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The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency are...
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Related Experiment Video

Updated: May 15, 2026

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes
06:25

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

Published on: February 23, 2024

Empirical mode decomposition analysis for visual stylometry.

James M Hughes1, Dong Mao, Daniel N Rockmore

  • 1Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA. hughes@cs.dartmouth.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 5, 2013
PubMed
Summary
This summary is machine-generated.

Empirical Mode Decomposition (EMD) analysis offers new quantitative tools for visual stylometry. This method enables effective Support Vector Machine (SVM) classifiers for comparing artistic styles, showing promise for art analysis.

Related Experiment Videos

Last Updated: May 15, 2026

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes
06:25

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

Published on: February 23, 2024

Area of Science:

  • Art History
  • Computer Science
  • Data Analysis

Background:

  • Visual stylometry aims to quantitatively measure and compare individual artistic styles.
  • Existing methods may lack the precision needed for nuanced style analysis.
  • Empirical Mode Decomposition (EMD) is a signal processing technique adaptable for image analysis.

Purpose of the Study:

  • To introduce a novel application of Empirical Mode Decomposition (EMD) for visual stylometry.
  • To develop and evaluate EMD-based image analysis for creating stylometric classifiers.
  • To test the efficacy of EMD-based methods on artworks by Pieter Bruegel the Elder and Rembrandt van Rijn.

Main Methods:

  • A new image-based Empirical Mode Decomposition (EMD) analysis was developed.
  • The output of the EMD analysis was used to train Support Vector Machine (SVM) classifiers.
  • Classifiers were tested on digital images of authentic and imitated artworks.

Main Results:

  • The developed EMD-based method successfully formed the basis for effective SVM stylometric classifiers.
  • Positive results were achieved when analyzing works attributed to Pieter Bruegel the Elder and Rembrandt van Rijn.
  • The study demonstrated the potential of EMD for distinguishing artistic styles.

Conclusions:

  • Empirical Mode Decomposition (EMD) analysis is a promising technique for visual stylometry.
  • EMD-based methods can provide quantitative tools for measuring and comparing individual artistic styles.
  • This approach holds potential for broader applications in art historical research and authentication.