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

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by
Same author

Data-Driven Approach to Dynamic Resting State Functional Connectivity in Post-Traumatic Stress Disorder: An ENIGMA-PGC PTSD Study.

Human brain mapping·2025
Same author

Genomic and Transcriptomic Signatures of SETD1A Disruption in Human Excitatory Neuron Development and Psychiatric Disease Risk.

bioRxiv : the preprint server for biology·2025
Same author

CalciumZero: a toolbox for fluorescence calcium imaging on iPSC derived brain organoids.

Brain informatics·2025
Same author

Characteristics of Adolescents with and without a Family History of Substance Use Disorder from a Minority Cohort.

Children (Basel, Switzerland)·2024
Same author

Socioeconomic factors, sleep timing and duration, and amygdala resting-state functional connectivity in children.

Frontiers in psychiatry·2024
Same author

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Frontiers in psychiatry·2024

Related Experiment Video

Updated: Jun 1, 2026

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

A hierarchical probabilistic model for rapid object categorization in natural scenes.

Xiaofu He1, Zhiyong Yang, Joe Z Tsien

  • 1Brain and Behavior Discovery Institute, Georgia Health Sciences University, Augusta, Georgia, United States of America.

Plos One
|June 8, 2011
PubMed
Summary

A new hierarchical probabilistic model enables rapid object categorization in natural scenes, mimicking human abilities. This model uses coarse probability distributions (PDs) of object structures, requiring minimal training and achieving near human-level performance in identifying animals and cars.

More Related Videos

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Related Experiment Videos

Last Updated: Jun 1, 2026

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

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Area of Science:

  • Cognitive Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Humans rapidly categorize objects in complex scenes (100-150 ms).
  • Existing computational models often require extensive training and high-dimensional feature spaces.
  • Current models frequently categorize scenes by context rather than object features, leaving human rapid categorization mechanisms unclear.

Purpose of the Study:

  • To develop a computational model for rapid object categorization in natural scenes.
  • To elucidate the mechanisms underlying human rapid scene categorization.
  • To overcome limitations of existing models in object localization and context-dependent categorization.

Main Methods:

  • Developed a hierarchical probabilistic model representing object categories with coarse probability distributions (PDs).
  • Object categories are defined by PDs of geometry, spatial configuration of parts, and natural object structures.
  • Categorization is performed via statistical inference on these PDs.

Main Results:

  • The model achieved near human-level performance in categorizing animals and cars in natural scenes.
  • The model demonstrated robustness with a small number of structures (∼100) and minimal training.
  • The model successfully located objects within scenes, unlike many context-focused models.

Conclusions:

  • Coarse probability distributions of object categories based on natural object structures may underlie human rapid scene categorization.
  • Statistical inference on these PDs offers an efficient and effective approach to object recognition.
  • The model provides a plausible explanation for human rapid categorization and localization abilities.