Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 7, 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

Unsupervised texture classification using vector quantization and deterministic relaxation neural network.

P P Raghu1, R Poongodi, B Yegnanarayana

  • 1Centre for Syst. and Devices, Indian Inst. of Technol., Madras.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Association between Physical Activity and Menopausal Symptoms among Midlife Women: A Cross-sectional Study in Rural Nagercoil and Kanyakumari Districts, Tamil Nadu, India.

Indian journal of public health·2026
Same author

Rice Husk-Derived Engineered Nanocellulose at Nano-Geo Interfaces for Mitigating Anthropogenic Heavy Metal Contamination.

Environmental geochemistry and health·2026
Same author

Processing group delay spectrograms for study of formant and harmonic contours in speech signals.

The Journal of the Acoustical Society of America·2024
Same author

Analysis of phase derivatives of speech signals.

The Journal of the Acoustical Society of America·2022
Same author

Group delay spectrogram of speech signals without phase wrapping.

The Journal of the Acoustical Society of America·2022
Same author

Analysis of aperiodicity in artistic Noh singing voice using an impulse sequence representation of excitation source.

The Journal of the Acoustical Society of America·2020

This study introduces an unsupervised neural network for textured image classification using 2-D Gabor filters and image constraints. It effectively classifies textures by iteratively refining features and determining the optimal number of classes.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Unsupervised texture classification remains challenging.
  • Extracting robust texture features is crucial for image analysis.
  • Existing methods often require labeled data, limiting their applicability.

Purpose of the Study:

  • To develop an unsupervised neural network for classifying textured images.
  • To utilize image-specific constraints for improved classification accuracy.
  • To extract effective texture features using Gabor filters and wavelet bases.

Main Methods:

  • Employing a neural network architecture with feature quantization, partition, and competition.
  • Using two-dimensional (2-D) Gabor filters for texture feature extraction.

Related Experiment Videos

Last Updated: Jul 7, 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

  • Implementing a deterministic relaxation procedure for iterative network and codevector adjustment.
  • Utilizing a modified Hubert index for determining the optimal number of texture classes.

    • Main Results:

    • Achieved unsupervised classification of textured images.
    • Demonstrated the effectiveness of Gabor filters for texture feature extraction.
    • Successfully employed image-specific constraints within the neural network model.
    • Determined the optimal number of texture classes using a cluster validity measure.

    Conclusions:

    • The proposed neural network effectively classifies textured images in an unsupervised manner.
    • The combination of Gabor filters and image-specific constraints yields robust texture features.
    • The deterministic relaxation procedure ensures convergence to a stable classification state.
    • The modified Hubert index provides a reliable method for determining the number of texture classes.