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Related Experiment Video

Updated: May 24, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Fast semantic diffusion for large-scale context-based image and video annotation.

Yu-Gang Jiang¹, Qi Dai, Jun Wang

¹School of Computer Science, Fudan University, Shanghai, China. ygj@fudan.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 21, 2012

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This study introduces semantic diffusion, a novel method for image and video annotation. It refines initial annotations using graph diffusion, significantly improving accuracy by 10+%.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Context information is crucial for visual recognition.
Large-scale image and video annotation presents significant challenges.
Existing graph-based methods focus on data sample relations.

Purpose of the Study:

To propose a novel and efficient semantic diffusion approach for large-scale image and video annotation.
To refine initial semantic concept annotations using graph diffusion.
To improve annotation accuracy and adapt to domain changes.

Main Methods:

Utilizing semantic diffusion, a graph diffusion technique.
Constructing a semantic graph where concepts are nodes and affinities are edge weights.

Related Experiment Videos

Last Updated: May 24, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Refining annotations for consistency and smoothness over the semantic graph.

Main Results:

Achieved consistent and significant performance gains exceeding 10% on both image and video datasets.
Demonstrated the approach's capability to simultaneously improve annotation accuracy.
Showcased adaptation to new test data, handling domain shifts effectively.

Conclusions:

Semantic diffusion offers a powerful and efficient solution for large-scale visual annotation.
The method effectively leverages semantic context for improved accuracy.
The approach successfully addresses domain adaptation challenges in visual recognition tasks.