{"title":"Image classification using local binary pattern operators for static images","authors":"Oana Astrid Vatamanu, Mircea Jivulescu","doi":"10.1109/SACI.2013.6608962","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":304729,"journal":{"name":"2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI.2013.6608962","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
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.