Vectorial Image Representation for Image Classification

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.

Keywords

• Vectorial Image Representation on the Texture Space (VIR-TS)
• T → %22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> texture unit <named-content content-type="inline-formula"><inline-formula> <mml:math id="mm1005"> <mml:semantics> <mml:mover accent="true"> <mml:mi>T</mml:mi> <mml:mo>→</mml:mo> </mml:mover> </mml:semantics> </mml:math> </inline-formula></named-content>
• homogeneous equation system
• multiclass classifier
• digital image recognition