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

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...
Concepts and Prototypes01:24

Concepts and Prototypes

The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
Visual Agnosia01:12

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round end"...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
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:
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

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

Sort by
Same author

TaxaScope: a container-native, visualization-centric workstation for genome-based bacterial taxonomy.

Frontiers in microbiology·2026
Same author

Histology-specific ADC target landscapes in ovarian cancer and therapy-associated antigen downshift after ADC exposure.

Gynecologic oncology·2026
Same author

Cold coagulation revisited: a strategy for high-grade cervical intraepithelial neoplasia in reproductive-age women.

Obstetrics & gynecology science·2026
Same author

Overall survival with relacorilant and nab-paclitaxel in patients with platinum-resistant ovarian cancer (ROSELLA): a phase 3 randomised controlled trial.

Lancet (London, England)·2026
Same author

Comparison Between Catheter-Directed Sclerotherapy and Surgical Removal of Large Ovarian Endometriomas: A Retrospective, Single-Center Observational Study.

Journal of clinical medicine·2026
Same author

Chlamy_ChloroPred: a deep learning-based, highly accurate binary classifier for chloroplast protein prediction in the model microalga, <i>Chlamydomonas reinhardtii</i>, with potential cross-proteome versatility.

Frontiers in microbiology·2026
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: 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

Object-graphs for context-aware visual category discovery.

Yong Jae Lee1, Kristen Grauman

  • 1Department of Electrical and Computer Engineering, The University of Texas at Austin, ACES 3.302, 1 University Station C0803, Austin, TX 78712, USA. yjlee0222@utexas.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 15, 2011
PubMed
Summary
This summary is machine-generated.

Leveraging knowledge of known object categories improves the discovery of new visual categories in unlabeled images. This method enhances unsupervised category discovery, especially in complex, cluttered scenes.

More Related Videos

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

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

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Unsupervised visual category discovery aims to identify recurring objects without human supervision.
  • Existing methods struggle with cluttered scenes due to a lack of prior information.
  • Accurate discovery in unsegmented, unlabeled images remains a challenge.

Purpose of the Study:

  • To leverage prior knowledge of learned categories for more accurate visual category discovery.
  • To address challenges in estimating category familiarity in complex image data.
  • To improve the detection of new visual categories in unlabeled images.

Main Methods:

  • Introduction of a novel object-graph descriptor encoding 2D and 3D spatial layouts of object co-occurrence.
  • Modeling the interaction between known and unknown objects within an image.
  • Utilizing familiar object cues to aid in the detection of novel categories.

Main Results:

  • Demonstrated clear improvements in visual category discovery over appearance-based baselines.
  • Showcased enhanced accuracy in cluttered scenes with multiple objects.
  • Validated the approach on several benchmark datasets.

Conclusions:

  • Prior knowledge of object categories significantly enhances unsupervised visual discovery.
  • The proposed object-graph descriptor effectively models spatial relationships for improved category detection.
  • This approach offers a more robust solution for mining new visual categories from unlabeled data.