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Related Experiment Video

Updated: May 18, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Learning image similarity from Flickr groups using fast kernel machines.

Gang Wang1, Derek Hoiem, David Forsyth

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. wanggang@ntu.edu.sg

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 22, 2012
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel image similarity measurement using Flickr image groups and a fast Support Vector Machine (SVM) training algorithm. The approach enhances image matching, retrieval, and classification accuracy compared to traditional methods.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Analysis

Background:

  • Measuring image similarity is crucial in computer vision.
  • Existing methods often rely on conventional visual features, which may not capture nuanced similarities.
  • Online image communities like Flickr offer rich, diverse data for learning image relationships.

Purpose of the Study:

  • To develop a novel method for measuring image similarity by leveraging data from online Flickr image groups.
  • To introduce an efficient training algorithm for Support Vector Machine (SVM) classifiers used in this similarity measure.
  • To evaluate the proposed approach's performance against conventional methods in image matching, retrieval, and classification tasks.

Main Methods:

  • Utilized 103 Flickr groups to train one-versus-all multiclass classifiers.

Related Experiment Videos

Last Updated: May 18, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

  • Employed SVM with a histogram intersection kernel for classification.
  • Developed the Stochastic Intersection Kernel MAchine (SIKMA) training algorithm for rapid training on large datasets.
  • Computed image similarity based on the distance between feature vectors derived from classifier responses.

    • Main Results:

    • The proposed image similarity measure demonstrated superior performance on the Corel and PASCAL VOC 2007 datasets.
    • Achieved better results in image matching, retrieval, and classification compared to conventional visual features.
    • The SIKMA algorithm enabled accurate SVM training on tens of thousands of examples within minutes, outperforming linear classifiers.

    Conclusions:

    • Learning image similarity from online image groups provides a robust and effective approach.
    • The SIKMA algorithm significantly improves the efficiency and scalability of training kernel classifiers.
    • This method offers a promising alternative for various computer vision applications requiring accurate image similarity assessment.