# Vectorial Image Representation for Image Classification

## TL;DR

This paper introduces a new method to represent images as vectors based on texture, which is used for image classification with promising results.

## Contribution

The novel contribution is the VIR-TS transformation, which converts images into texture-based vectors for classification.

## Key findings

- The VIR-TS transformation effectively captures local texture characteristics for image classification.
- The proposed method achieved effective performance in classifying tree bark images.
- The parameter λ used in solving the homogeneous equation system does not impact classification results.

## Abstract

This paper proposes the transformation S→C→, where S is a digital gray-level image and C→ is a vector expressed through the textural space. The proposed transformation is denominated Vectorial Image Representation on the Texture Space (VIR-TS), given that the digital image S is represented by the textural vector C→. This vector C→ contains all of the local texture characteristics in the image of interest, and the texture unit T→ entertains a vectorial character, since it is defined through the resolution of a homogeneous equation system. For the application of this transformation, a new classifier for multiple classes is proposed in the texture space, where the vector C→ is employed as a characteristics vector. To verify its efficiency, it was experimentally deployed for the recognition of digital images of tree barks, obtaining an effective performance. In these experiments, the parametric value λ employed to solve the homogeneous equation system does not affect the results of the image classification. The VIR-TS transform possesses potential applications in specific tasks, such as locating missing persons, and the analysis and classification of diagnostic and medical images.