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Related Concept Videos

Visual System01:26

Visual System

1.7K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Related Experiment Video

Updated: Jan 17, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Scene Graph and Natural Language-Based Semantic Image Retrieval Using Vision Sensor Data.

Jaehoon Kim1, Byoung Chul Ko1

  • 1Department of Computer Engineering, Keimyung University, Daegu 42601, Republic of Korea.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new graph neural network (GNN) approach for text-based image retrieval, improving accuracy by comparing semantic and scene graphs for better understanding of visual content.

Keywords:
graph neural networkgraph similarity learningscene graph generationsemantic image retrievalsubgraph extractionvision sensor

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Text-based image retrieval often uses keyword matching, which struggles with semantic nuances and limited query information.
  • Existing methods lack accuracy when dealing with complex scenes or novel sentences, failing to capture full contextual meaning.

Purpose of the Study:

  • To develop a novel approach for text-based image retrieval that overcomes the limitations of keyword matching.
  • To enhance retrieval accuracy by enabling quantitative comparison between textual descriptions and visual scene content.

Main Methods:

  • Transforming sentences into semantic graphs and images into scene graphs.
  • Utilizing a graph neural network (GNN) to learn node and edge features, generating graph embeddings for comparison.
  • Implementing a contrastive GNN framework with hard negative mining to match semantic and scene graphs.

Main Results:

  • The proposed GNN-based method achieved a top nDCG@50 score of 0.745 on the Visual Genome dataset.
  • Demonstrated an improvement of approximately 7.7 percentage points compared to random sampling with full graphs.
  • Successfully retrieved semantically relevant images by structurally interpreting complex scenes.

Conclusions:

  • The novel GNN-based approach effectively addresses limitations in text-based image retrieval.
  • Quantitative comparison of semantic and scene graphs significantly enhances retrieval accuracy.
  • Structural interpretation of scenes via graph embeddings enables robust image retrieval from natural language queries.