Vectorial Image Representation for Image Classification

Abstract

This paper proposes the transformation  (Formula presented.), where S is a digital gray-level image and  (Formula presented.)  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  (Formula presented.). This vector  (Formula presented.)  contains all of the local texture characteristics in the image of interest, and the texture unit  (Formula presented.)  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  (Formula presented.)  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.