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

Gestalt Principles of Perception01:21

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
Perceptual Constancy01:12

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
Information Processing Approach01:30

Information Processing Approach

The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is also...
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:
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category, whereas...

You might also read

Related Articles

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

Sort by
Same author

Rapid Artificial Intelligence Autoplanning Rivals Manual Expert Planning for Cervical Brachytherapy.

Practical radiation oncology·2026
Same author

Macula Spatial Patterns and Their Association With Central Visual Field Progression in Glaucoma Using Artificial Intelligence.

Journal of glaucoma·2026
Same author

Integration of single-click, AI-based brachytherapy auto-planning for cervical cancer within a treatment planning system.

Brachytherapy·2025
Same author

Deep Learning Estimation of 24-2 Visual Field Map From Optic Nerve Head Optical Coherence Tomography Angiography.

Journal of glaucoma·2025
Same author

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

Medical physics·2024
Same author

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements.

American journal of ophthalmology·2023
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 30, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

Holistic context models for visual recognition.

Nikhil Rasiwasia1, Nuno Vasconcelos

  • 1Statistical Visual Computing Laboratory, Department of Electrical and Computer Engineering, University of California, San Diego, EBU 1, Room 5512, 9500 Gilman Drive, La Jolla, CA 92093-0407, USA. nikux@ucsd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 17, 2011
PubMed
Summary
This summary is machine-generated.

A new context modeling framework uses object and scene co-occurrence probabilities for improved image understanding. This approach enhances scene classification and image retrieval accuracy, outperforming existing methods.

More Related Videos

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 30, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Context modeling is crucial for image understanding.
  • Existing methods often struggle with ambiguity and noise in image data.
  • Robust appearance classifiers are available but require contextual integration.

Purpose of the Study:

  • To propose a novel, simple framework for context modeling.
  • To leverage object-scene co-occurrence probabilities for enhanced image representation.
  • To improve performance in tasks like scene classification and image retrieval.

Main Methods:

  • Developed a two-layer probabilistic modeling framework.
  • Images represented by posterior probabilities using bag-of-features.
  • First layer maps images to a semantic space based on concept probabilities.
  • Second layer models concept distributions to handle noise.

Main Results:

  • The framework effectively captures the contextual 'gist' of natural images.
  • Contextual classifiers significantly outperform appearance-based classifiers.
  • Demonstrated superior results in scene classification and image retrieval benchmarks.

Conclusions:

  • The proposed context modeling framework is effective and robust.
  • It offers significant improvements over existing approaches.
  • The method provides a powerful tool for advanced image analysis.