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Vectorial Image Representation for Image Classification.

Maria-Eugenia Sánchez-Morales1, José-Trinidad Guillen-Bonilla2, Héctor Guillen-Bonilla3

  • 1Departamento de Ciencias Tecnológicas, Centro Universitario de la Ciénega, Universidad de Guadalajara, Av. Universidad No. 1115, Lindavista, Ocotlán 47810, Jalisco, Mexico.

Journal of Imaging
|February 23, 2024
PubMed
Summary

This study introduces Vectorial Image Representation on the Texture Space (VIR-TS), a new method to represent digital images using textural vectors. VIR-TS effectively captures local texture characteristics for image classification tasks.

Keywords:
Vectorial Image Representation on the Texture Space (VIR-TS)digital image recognitionhomogeneous equation systemmulticlass classifiertexture unit  T →

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Digital image analysis often relies on extracting meaningful features.
  • Representing complex image textures efficiently is a key challenge in computer vision.

Purpose of the Study:

  • To propose a novel transformation, Vectorial Image Representation on the Texture Space (VIR-TS), for digital gray-level images.
  • To develop a new texture space classifier utilizing the proposed transformation for multi-class recognition.

Main Methods:

  • The Vectorial Image Representation on the Texture Space (VIR-TS) transformation converts a digital image (S) into a textural vector (C→).
  • The textural vector (C→) encapsulates local texture characteristics, derived from solving a homogeneous equation system.
  • A new multi-class classifier is proposed, using the textural vector (C→) as its feature vector.

Main Results:

  • Experimental deployment on digital tree bark images demonstrated effective performance in recognition tasks.
  • The classification results were found to be independent of the parametric value (λ) used in solving the homogeneous equation system.

Conclusions:

  • The VIR-TS transformation provides an effective method for image representation based on texture.
  • The proposed method shows potential for applications in areas like missing person detection and medical image analysis.