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Related Experiment Video

Updated: Jul 1, 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

Annotating images by mining image search results.

Xin-Jing Wang¹, Lei Zhang, Xirong Li

¹Microsoft Research Asia, 4F Sigma Center, 49 Zhichun Road, Haidan District, Beijing 100190, PR China. xjwang@microsoft.com

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 13, 2008

Summary

This summary is machine-generated.

This study introduces a novel model-free image annotation method using search result mining. The approach efficiently annotates images in under a second without requiring training data, enhancing practical applications.

Related Experiment Videos

Last Updated: Jul 1, 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

Area of Science:

Computer Vision
Machine Learning
Data Mining

Background:

Traditional image annotation remains impractical despite extensive research.
Existing methods often require large, labeled training datasets.

Purpose of the Study:

To develop a novel, model-free image annotation technique.
To improve the efficiency and effectiveness of image annotation.

Main Methods:

A data-driven approach mining search results from 2.4 million images.
Utilizing a divide-and-conquer framework with optional query keywords.
Employing hash codes for visual features and a distributed system for real-time processing.

Main Results:

Achieved real-time annotation in under 1 second.
Demonstrated effectiveness and efficiency on real-world and benchmark datasets.
Showcased scalability and robustness to outliers.

Conclusions:

The proposed model-free image annotation method is practical and efficient.
The approach enables unlimited vocabulary annotation without training data.
The system is robust and scalable for diverse image annotation tasks.