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Related Experiment Video

Updated: May 3, 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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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

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Retrieval-based face annotation by weak label regularized local coordinate coding.

Dayong Wang1, Steven C H Hoi1, Ying He1

  • 1Nanyang Technological University, Singapore.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Weak Label Regularized Local Coordinate Coding (WLRLCC) method for auto face annotation using web images. The technique effectively handles noisy labels from retrieved images, improving accuracy in facial recognition tasks.

Related Experiment Videos

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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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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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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Auto face annotation is crucial for organizing large image datasets and enabling applications like facial recognition.
  • Existing methods struggle with the inherent noise and incompleteness of labels in large-scale web-scraped facial image datasets.

Purpose of the Study:

  • To develop an effective retrieval-based auto face annotation scheme using massive, freely available web facial images.
  • To address the challenges of accurately retrieving similar facial images and exploiting noisy or incomplete labels from web data.

Main Methods:

  • A Weak Label Regularized Local Coordinate Coding (WLRLCC) technique is proposed, combining local coordinate coding for sparse features with graph-based weak label regularization.
  • An efficient optimization algorithm is developed to solve the WLRLCC problem.
  • A sparse reconstruction scheme is utilized for the face annotation task.

Main Results:

  • The WLRLCC algorithm demonstrates efficacy in auto face annotation across multiple web facial image databases.
  • Extensive empirical studies validate the proposed method's performance.
  • Two large-scale databases, WDB and ADB, are constructed and shared publicly.

Conclusions:

  • The WLRLCC technique offers an effective solution for auto face annotation, particularly in scenarios with noisy web data.
  • An offline approximation scheme (AWLRLCC) is introduced for improved efficiency and scalability with comparable results.
  • The study contributes valuable datasets and a robust algorithm to the field of facial image analysis.