Image classification using local binary pattern operators for static images

This paper aims to present an image classification method using Local Binary Pattern techniques. Local Binary Pattern operator transforms an static image, at pixel level, into a matrix of labels. These labels - integer numbers - describe and characterise the original image at a much lower scale. The authors propose the use of labels as a global characteristic of an static image. These techniques can be applied to an image or to a group of images and the characterization is done through an array of values extracted by the algorithm. The application developed allows the characterization of an image or a set of images, determining the similarity between different images and the degree of belonging to a particular group. Vectors of values are required for more images and image groups and each vector is representing different textures and their classification. As a result it becomes possible that indexing images, take into account the content of the information present in the image.